Miracle-dz/UniFAR-FTUs huggingface dataset:
Endothelial Dysfunction and Elevated Blood Pressure in Mas Gene-Deleted Mice

Mas codes for a G protein–coupled receptor that is implicated in angiotensin-(1-7) signaling. We studied the cardiovascular phenotype of Mas-deficient mice backcrossed onto the FVB/N genetic background using telemetry and found that they exhibit higher blood pressures compared with controls. These Mas−/− mice also had impaired endothelial function, decreased NO production, and lower endothelial NO synthase expression. Reduced nicotinamide-adenine dinucleotide phosphate oxidase catalytic subunit gp91phox protein content determined by Western blotting was higher in Mas−/− mice than in controls, whereas superoxide dismutase and catalase activities were reduced. The superoxide dismutase mimetic, Tempol, decreased blood pressure in Mas−/− mice but had a minimal effect in control mice. Our results show a major cardiovascular phenotype in Mas−/− mice. Mas-deletion results in increased blood pressure, endothelial dysfunction, and an imbalance between NO and reactive oxygen species. Our animals represent a promising model to study angiotensin-(1-7)–mediated cardiovascular effects and to evaluate Mas agonistic compounds as novel cardioprotective and antihypertensive agents based on their beneficial effects on endothelial function.
Fast denoising of surface meshes with intrinsic texture

We describe a fast, dynamic, multiscale iterative method that is designed to smooth, but not over-smooth, noisy triangle meshes. Our method not only preserves sharp features but also retains visually meaningful fine-scale components or details, referred to as intrinsic texture. An anisotropic Laplacian (AL) operator is first developed. It is then embedded in an iteration that gradually and adaptively increases the importance of data fidelity, yielding a highly efficient multiscale algorithm (MSAL) that is capable of handling both intrinsic texture and mesh-sampling irregularity without any significant cost increase.
Learning, Goals, and Learning Goals

This chapter contains sections titled: Why Goals? , An Everyday Example, Toward a Planful Model of Learning, A Framework for Goal-Driven Learning, Major Issues in Goal-Driven Learning, What is a Goal? , Types of Goals, Role of Goals in Learning, Pragmatic Implications of Goal-Driven Learning, Summary, Acknowledgments, Notes, References
Mind the Gap? Perceptions and Realities of Sexualities Education for Ordinands

This article reports the findings of a small-scale qualitative research project which sought to discover how sexualities education was delivered in Anglican Theological Education Institutions in the UK. With a background of cultural and ecclesial change, as well as the adoption of a single university to validate the majority of theological education courses, the authors build on work done previously to determine the presence of gaps or dissonances between policy and practice. Analysis of on-line questionnaires and semi-structured interviews suggests continuing contestations around sexualities, and diverse opinion even within the same institution. By exploring data within a postmodern methodological framework under the headings of formal education, informal education and the discernment process, we note more congruence than previously within Colleges and Courses, but a diversity of experiences in different dioceses. We recognize the challenges of theological education in a disputed environment, and recommend some changes to the selection process.
Bulk universality for Wigner hermitian matrices with subexponential decay

We consider the ensemble of $n \times n$ Wigner hermitian matrices $H = (h_{\ell k})_{1 \leq \ell,k \leq n}$ that generalize the Gaussian unitary ensemble (GUE). The matrix elements $h_{k\ell} = \bar h_{\ell k}$ are given by $h_{\ell k} = n^{-1/2} (x_{\ell k} + \sqrt{-1} y_{\ell k})$, where $x_{\ell k}, y_{\ell k}$ for $1 \leq \ell < k \leq n$ are i.i. d. random variables with mean zero and variance 1/2, $y_{\ell\ell}=0$ and $x_{\ell \ell}$ have mean zero and variance 1. We assume the distribution of $x_{\ell k}, y_{\ell k}$ to have subexponential decay. In a recent paper, four of the authors recently established that the gap distribution and averaged $k$-point correlation of these matrices were \emph{universal} (and in particular, agreed with those for GUE) assuming additional regularity hypotheses on the $x_{\ell k}, y_{\ell k}$. In another recent paper, the other two authors, using a different method, established the same conclusion assuming instead some moment and support conditions on the $x_{\ell k}, y_{\ell k}$. In this short note we observe that the arguments of these two papers can be combined to establish universality of the gap distribution and averaged $k$-point correlations for all Wigner matrices (with subexponentially decaying entries), with no extra assumptions.
RTT Prediction in Heavy Tailed Networks

TCP is the most used transport protocol in the Internet and it relies on RTT (Round Trip Time) predictions for the retransmission control algorithm. Most of the algorithms reported in the literature consider memoryless traffic characteristics and do not study the performance under heavy tailed scenarios present in the Internet. In this paper, an algorithm for RTT prediction in a heavy-tailed environment is introduced, and it is shown to follow closely and accurately the actual RTT. The proposed algorithm is simple and permits online implementations. Results are compared with those obtained with other methodologies for real trace sets. It is shown that the proposed algorithm leads to a lower prediction error.
Self-Love or Other-Love? Explicit Other-Preference but Implicit Self-Preference

Do humans prefer the self even over their favorite other person? This question has pervaded philosophy and social-behavioral sciences. Psychology’s distinction between explicit and implicit preferences calls for a two-tiered solution. Our evolutionarily-based Dissociative Self-Preference Model offers two hypotheses. Other-preferences prevail at an explicit level, because they convey caring for others, which strengthens interpersonal bonds–a major evolutionary advantage. Self-preferences, however, prevail at an implicit level, because they facilitate self-serving automatic behavior, which favors the self in life-or-die situations–also a major evolutionary advantage. We examined the data of 1,519 participants, who completed an explicit measure and one of five implicit measures of preferences for self versus favorite other. The results were consistent with the Dissociative Self-Preference Model. Explicitly, participants preferred their favorite other over the self. Implicitly, however, they preferred the self over their favorite other (be it their child, romantic partner, or best friend). Results are discussed in relation to evolutionary theorizing on self-deception.
iFlatLFS: Performance optimization for accessing massive small files

The processing of massive small files is a challenge in the design of distributed file systems. Currently, the combined-block-storage approach is prevalent. However, the approach employs traditional file systems like ExtFS and may cause inefficiency for random access to small files. This paper focuses on optimizing the performance of data servers in accessing massive small files. We present a Flat Lightweight File System (iFlatLFS) to manage small files, which is based on a simple metadata scheme and a flat storage architecture. iFlatLFS aims to substitute the traditional file system on data servers that are mainly used to store small files, and it can greatly simplify the original data access procedure. The new metadata proposed in this paper occupies only a fraction of the original metadata size based on traditional file systems. We have implemented iFlatLFS in CentOS 5.5 and integrated it into an open source Distributed File System (DFS), called Taobao FileSystem (TFS), which is developed by a top B2C service provider, Alibaba, in China and is managing over 28.6 billion small photos. We have conducted extensive experiments to verify the performance of iFlatLFS. The results show that when the file size ranges from 1KB to 64KB, iFlatLFS is faster than Ext4 by 48% and 54% on average for random read and write in the DFS environment, respectively. Moreover, after iFlatLFS is integrated into TFS, iFlatLFS-based TFS is faster than the existing Ext4-based TFS by 45% and 49% on average for random read access and hybrid access (the mix of read and write accesses), respectively.
Sports–Politics Puzzle in China’s Macau

Abstract Sports serve politics through three major means: diplomacy, national identity/unity and regime legitimacy. This paper shows that sports, to a great extent, cannot serve Macau politics in meeting the three social purposes of sports mentioned above. Although mega events can serve the purposes of diplomacy that are conducive to improving Macau’s external relationships and international image, they are counter-productive to the Macau people’s sense of belonging to the region as well as to the legitimacy of the Macau Special Administrative Region (MSAR) regime. Nevertheless, sports have their political function in the unique MSAR political system as well, creating an arena for political power in Macau, primarily because political elites compete for the support of sports associations in political elections. Due to the special setting of Macau’s political system, sports associations have the right to select representatives for the legislature as well as to elect the MSAR’s Chief Executive. This results in collusion between politicians and sports associations.
Design of intelligent business applications based in BPM and MDE

An architecture has been designed for the generation of intelligent applications of business, that allows to confront the increasing need on the part of the business users to adapt their processes to the constant change. Up to the moment all the architectures based to a great extent on SOA allow to modify the processes in a short period of time, but we go beyond and give the possibility to the business user of modifying their processes. To confront such a challenge, we rely on the fundamental use of two technologies: BPM (Business Process Modeling) and MDE (Model Driven Engineering). Inside these technologies we focus on the creation of a business process extended notation that is simpler, capable of being understood and modified by the business user to achieve the proposed goal.
Region matching and similarity enhancing for image retrieval

Many image retrieval systems adopt the bag-of-words model and rely on matching of local descriptors. However, these descriptors of keypoints, such as SIFT, may lead to false matches, since they do not consider the contextual information of the keypoints. In this paper, we incorporate the cues of meaningful regions where local descriptors are extracted. We describe a matching region estimation (MRE) method to find appropriate matching regions for local descriptor matching pairs. Then the region matching quality is evaluated and the true matched regions will enhance the similarity of local descriptors. Consequently, the image retrieval accuracy can be improved. Extensive experiments on benchmark datasets show the effectiveness of our method and our result compares favorably with the state-of-the-art.
News Analytics and Sentiment Analysis to Predict Stock Price Trends

Business news carries varied information of different companies. But in this rapidly moving world the number of news sources present is uncountable, and it’s humanly impossible to read and find all relevant information in the form of news to draw a conclusion timely to make an investment plan that returns maximum profit. In this paper, we have proposed a predictive model to predict sentiment around stock price. First the relevant real time news headlines and press-releases have been filtered from the large set of business news sources, and then they have been analyzed to predict the sentiment around companies. In order to find correlation between sentiment predicted from news and original stock price and to test efficient market hypothesis, we plot the sentiments of 15 odd companies over a period of 4 weeks. Our result shows an average accuracy score for identifying correct sentiment of around 70.1%. We also have plotted the errors of prediction for different companies which have brought out the RMSE and MAE of 30.3% and 30.04% respectively and an enhanced F1 factor of 78.1%. The comparison between positive sentiment curve and stock price trends reveals 67% co-relation between them, which indicates towards existence of a semi-strong to strong efficient market hypothesis. Keywords— stock price trends, prediction model, knowledge discovery, sentiment analysis, market trends, news analytics, efficient market hypothesis.
Generating leads with sequential persuasion: should sales influence tactics be consistent or complementary?

Firms are becoming increasingly systematic in sales lead generation activities and recognize that multiple interactions are often required to generate leads and to reassure buyers of the benefits of an offering. However, the literature is contradictory regarding what types of persuasive messages should be used across these multiple interactions. This paper investigates what combination of sales influence tactics (SITs) should be used when customers are approached more than once regarding the same offering, that is, sequentially. Should the SITs be consistent (the same), or complementary (different)? Consistent vs. complementary SIT approaches were tested in two field experiments, one with an electronics manufacturer in a single communication channel setting and one with a financial services provider in a cross-channel setting. The results supported our hypothesis and indicated that the use of complementary SITs across interactions, regardless of the order, increases lead conversion.
What Are They Blogging About? Personality, Topic and Motivation in Blogs

Personal weblogs (blogs), provide individuals with the opportunity to write freely and express themselves online in the presence of others. In such situations, what do bloggers write about, and what are their motivations for blogging? Using a large blog corpus annotated with the LIWC text analysis program, we examine the content of blogs to provide insight into the role of personality in motivation for blogging. As predicted, we find that highly Neurotic authors use blogs to serve a cathartic or auto-therapeutic function, and reflect mainly upon themselves and negative emotions. Highly Extraverted blog authors, as expected, use blogs to document their lives at a high level, and uniquely interact directly with the reader. Additionally Extraverts use blogs to vent both positive and negative emotions. Bloggers who are high scorers on the Openness trait are more concerned with leisure activities, although they are more evaluative than intellectual, whereas high Conscientiousness bloggers tend to report daily life – and work – around them. As in other contexts, expressing positive rather than negative emotions is associated with high Agreeableness, but that trait is associated with self reference to a greater degree in blogs than elsewhere. In general, findings are consistent with other contexts indicating that bloggers tend to adapt to the possibilities of the medium, rather than try to present themselves differently.
Using Prefetching to Hide Lock Acquisition Latency in Distributed Virtual Shared Memory Systems

Synchronization overhead may limit the number of applications that can take advantage of a shared-memory abstraction on top of emerging network of workstation organizations. While the programmer could spend additional efforts into getting rid of such overhead by restructuring the computation, this paper focuses on a simpler approach where the overhead of lock operations is hidden through lock prefetch annotations. Our approach aims at hiding the lock acquisition latency by prefetching the lock ahead of time. This paper presents a compiler approach which turned out to automatically insert lock prefetching annotations successfully in five out of eight applications. In the other, we show that hand insertion could be done fairly easily without any prior knowledge about the applications. We also study the performance improvements of this approach in detail by considering network of workstation organizations built from uniprocessor as well as symmetric multiprocessor nodes for emerging interconnect technologies such as ATM. It is shown that the significant latencies have a dramatic effect on the lock acquisition overhead and this overhead can be drastically reduced by lock prefetching. Overall, lock prefetching is a simple and effective approach to allow more fine-grained applications to run well on emerging network of workstation platforms.
Scene Image Synthesis from Natural Sentences Using Hierarchical Syntactic Analysis

Synthesizing a new image from verbal information is a challenging task that has a number of applications. Most research on the issue has attempted to address this question by providing external clues, such as sketches. However, no study has been able to successfully handle various sentences for this purpose without any other information. We propose a system to synthesize scene images solely from sentences. Input sentences are expected to be complete sentences with visualizable objects. Our priorities are the analysis of sentences and the correlation of information between input sentences and visible image patches. A hierarchical syntactic parser is developed for sentence analysis, and a combination of lexical knowledge and corpus statistics is designed for word correlation. The entire system was applied to both a clip-art dataset and an actual image dataset. This application highlighted the capability of the proposed system to generate novel images as well as its ability to succinctly convey ideas.
Modified gysel power divider for hyperthermia using microstrip to slotline transitions

An in-phase power divider with high power handling capability is presented. The proposed design is a modified structure of the traditional Gysel design. The modification includes using microstrip to slotline transitions to connect the input port to the two output ports with the aim to match the ports across wide band. As with the conventional design, the device uses an external isolation resistor, and thus it is suitable for high power microwave based applications. The proposed structure provides 53% fractional bandwidth (1.5-2.6 GHz) based on 15 dB of isolation as the reference. The simulated results show equal power division with 1 dB additional insertion loss, more than 15 dB of return loss at the input, 10 dB at the output ports, and 15 dB isolation while maintaining less than 10° phase imbalance between the output ports.
A Statistical Rule Learning Approach to Network Intrusion Detection

With the enormous growth of users reliance on the Internet, the need for secure and reliable computer networks also increases. A good security mechanism requires an Intrusion Detection System (IDS) in order to monitor security breaches when the prevention schemes are circumvented. To be able to react to different network attacks in changing environments, a generic and flexible detection system is of paramount importance. This paper presents a method that uses statistical features as the input to a rule learning technique. First, for extracting suitable features for intrusion detection, an entropy and volume- based approach is introduced. Then, for the classification task, a genetic-based rule learning technique that utilises an interval-based representation for statistical features of network traffic is proposed. Two sources of data are used to evaluate this technique and to compare against other machine learning techniques. The results show that our proposed approach provides simple rulesets with competitive detection performance in comparison to other algorithms.
Asynchronous Trajectory Control for a Walking Robot Table of Contents

ACKNOWLEDGEMENTS I am indebted to my adviser, Professor Mark W. Spong, for introducing me to the tools of nonlinear control, for opening my eyes to the depth of the problem, and for giving me a chance to work in this area. Special credit belongs to my wife, Jiyeon, for her continuing support and patience. I also thank our parents for the time and dedication invested in our education and upbringing. Others who indirectly aided this thesis are Professor Bruce Reznick, whose " dot products and derivatives " helped inspire this control, and Professor Daniel Koditschek, whose guest presentation inspired Figure 4.1. Similarly, I appreciate the support of my peers in room 316TB and elsewhere for their helpful discussions and camaraderie.
Evaluating a GA-based approach to dynamic query approximation on an inference-enabled SPARQL endpoint

In this paper, we present an evaluation of our idea and its conceptual model on building endpoints having a mechanism to automatically reduce unwanted amount of inference computation by predicting its computational costs and allowing it to transform such a query into more speed optimized one by applying a GA-based query rewriting approach. Our preliminary evaluation shows the benefit on preventing unexpectedly long inference computations but keeping low variance of inference-enabled query executions by applying our query rewriting approach.
A Spring Model for Whole-Hand Virtual Grasping

We present a physically-based approach to grasping and manipulation of virtual objects that produces visually realistic results, addresses the problem of visual inter-penetration of hand and object models, and performs force rendering for force-feedback gloves, in a single framework. Our approach couples a simulation-controlled articulated hand model to tracked hand configuration using a system of linear and torsional virtual spring-dampers. We discuss an implementation of our approach that uses a widely available simulation tool for collision detection and response. We pictorially illustrate the resulting behavior of the virtual hand model and of grasped objects, discuss user behavior and difficulties encountered, and show that the simulation rate is sufficient for control of current force-feedback glove designs. We also present a prototype system for natural whole-hand interactions in a desktop-sized workspace.
Feature selection for improving opinion identification from web authors' posts

In the present article, we address the problem of automatic opinion identification of web users from movie reviews. Specifically, relying on six well-known machine learning algorithms, we investigate the effectiveness of feature selection in the improvement of the accuracy of opinion identification. The feature ranking is performed over a set of statistical, part-of-speech tagging and language model based features. In the experiments, we employed classification models based on decision trees, support vector machines and lazy-learning algorithms. The experimental evaluation performed on the publicly available Polarity Dataset v2.0 demonstrated that feature selection significantly improves the accuracy of opinion identification regardless of the type of machine learning algorithm used.
Radial thresholding to mitigate laser guide star aberrations on centre-of-gravity-based Shack–Hartmann wavefront sensors

Sodium laser guide stars (LGSs) are elongated sources due to the thickness and the finite distance of the sodium layer. The fluctuations of the sodium layer altitude and atom density profile induce errors on centroid measurements of elongated spots, and generate spurious optical aberrations in closed-loop adaptive optics (AO) systems. According to an analytical model and experimental results obtained with the University of Victoria LGS bench demonstrator, one of the main origins of these aberrations, referred to as LGS aberrations, is not the centre-of-gravity (CoG) algorithm itself, but the thresholding applied on the pixels of the image prior to computing the spot centroids. A new thresholding method, termed ‘radial thresholding’, is presented here, cancelling out most of the LGS aberrations without altering the centroid measurement accuracy.