Miracle-dz/UniFAR-FTUs huggingface dataset:
Setting Up Static Components for Investigating MANET Performance: A Simulation Case

MANETs are temporary networks, that spend less time to be deployed and less resources to keep up and running. MANETs can be implemented in disaster recovery sites, university campuses, military campaigns and so on. MANET nodes cooperate in an autonomous way by forwarding packets to each-other. Considering mobility of the terminals, the routes change dynamically so routing algorithms are very important for operation of MANETs. At a given time, not all the nodes can be mobile. Users can do some work, while sitting somewhere or stop for a rest. We can also consider a grid of static nodes, which will also support the mobile nodes. In this paper, we analyze the performance of a MANET considering different number of static nodes in the network. For the moving nodes we use Random Waypoint Mobility (RWM) model. We evaluate the performance by measuring the number of hops and throughput for one-flow traffic. For all scenarios, the average throughput is around 5 times lower than CBR of sent data. The number of hops is higher and has more oscillations for denser networks.
Maximum-LikelihoodSemiblind Equalization of Doubly Selective Channels Using the EM Algorithm

Maximum-likelihood semi-blind joint channel estimation and equalization for doubly selective channels and single-carrier systems is proposed. We model the doubly selective channel as an FIR filter where each filter tap is modeled as a linear combination of basis functions. This channel description is then integrated in an iterative scheme based on the expectation-maximization (EM) principle that converges to the channel description vector estimation. We discuss the selection of the basis functions and compare various functions sets. To alleviate the problem of convergence to a local maximum, we propose an initialization scheme to the EM iterations based on a small number of pilot symbols. We further derive a pilot positioning scheme targeted to reduce the probability of convergence to a local maximum. Our pilot positioning analysis reveals that for high Doppler rates it is better to spread the pilots evenly throughout the data block (and not to group them) even for frequency-selective channels. The resulting equalization algorithm is shown to be superior over previously proposed equalization schemes and to perform in many cases close to the maximum-likelihood equalizer with perfect channel knowledge. Our proposed method is also suitable for coded systems and as a building block for Turbo equalization algorithms.
Distal Degenerative Sensory Neuropathy in a Long-Term Type 2 Diabetes Rat Model

OBJECTIVE—Peripheral neuropathy associated with type 2 diabetes (DPN) is not widely modeled. We describe unique features of DPN in type 2 diabetic Zucker diabetic fatty (ZDF) rats. RESEARCH DESIGN AND METHODS—We evaluated the structural, electrophysiological, behavioral, and molecular features of DPN in ZDF rats and littermates over 4 months of hyperglycemia. The status of insulin signaling transduction molecules that might be interrupted in type 2 diabetes and selected survival-, stress-, and pain-related molecules was emphasized in dorsal root ganglia (DRG) sensory neurons. RESULTS—ZDF rats developed slowing of motor sciatic-tibial and sensory sciatic digital conduction velocity and selective mechanical allodynia with preserved thermal algesia. Diabetic sural axons, preserved in number, developed atrophy, but there was loss of large-calibre dermal and small-calibre epidermal axons. In diabetic rats, insulin signal transduction pathways in lumbar DRGs were preserved or had trends toward upregulation: mRNA levels of insulin receptor β-subunit (IRβ), insulin receptor substrate (IRS)-1, and IRS-2. The numbers of neurons expressing IRβ protein were also preserved. There were trends toward early rises of mRNA levels of heat shock protein 27 (HSP27), the α2δ1 calcium channel subunit, and phosphatidylinositol 3-kinase in diabetes. Others were unchanged, including nuclear factor-κB (NF-κB; p50/p105) and receptor for advanced glycosylation endproducts (RAGE) as was the proportion of neurons expressing HSP27, NF-κB, and RAGE protein. CONCLUSIONS—ZDF type 2 diabetic rats develop a distal degenerative sensory neuropathy accompanied by a selective long-term pain syndrome. Neuronal insulin signal transduction molecules are preserved.
Parameterization of dust emissions in the global atmospheric chemistry-climate model EMAC: impact of nudging and soil properties

Abstract. Airborne desert dust influences radiative transfer, atmospheric chemistry and dynamics, as well as nutrient transport and deposition. It directly and indirectly affects climate on regional and global scales. Two versions of a parameterization scheme to compute desert dust emissions are incorporated into the atmospheric chemistry general circulation model EMAC (ECHAM5/MESSy2.41 Atmospheric Chemistry). One uses a globally uniform soil particle size distribution, whereas the other explicitly accounts for different soil textures worldwide. We have tested these two versions and investigated the sensitivity to input parameters, using remote sensing data from the Aerosol Robotic Network (AERONET) and dust concentrations and deposition measurements from the AeroCom dust benchmark database (and others). The two versions are shown to produce similar atmospheric dust loads in the N-African region, while they deviate in the Asian, Middle Eastern and S-American regions. The dust outflow from Africa over the Atlantic Ocean is accurately simulated by both schemes, in magnitude, location and seasonality. Approximately 70% of the modelled annual deposition data and 70–75% of the modelled monthly aerosol optical depth (AOD) in the Atlantic Ocean stations lay in the range 0.5 to 2 times the observations for all simulations. The two versions have similar performance, even though the total annual source differs by ~50%, which underscores the importance of transport and deposition processes (being the same for both versions). Even though the explicit soil particle size distribution is considered more realistic, the simpler scheme appears to perform better in several locations. This paper discusses the differences between the two versions of the dust emission scheme, focusing on their limitations and strengths in describing the global dust cycle and suggests possible future improvements.
A pr 2 00 6 Intersecting hypersurfaces in AdS and Lovelock gravity

Colliding and intersecting hypersurfaces filled with matter (membranes) are studied in the Lovelock higher order curvature theory of gravity. Lovelock terms couple hypersurfaces of different dimensionalities, extending the range of possible intersection configurations. We restrict the study to constant curvature membranes in constant curvature AdS and dS background and consider their general intersections. This illustrates some key features which make the theory different to the Einstein gravity. Higher co-dimension membranes may lie at the intersection of co-dimension 1 hypersurfaces in Lovelock gravity; the hypersurfaces are located at the discontinuities of the first derivative of the metric, and they need not carry matter. The example of colliding membranes shows that general solutions can only be supported by (spacelike) matter at the collision surface, thus naturally conflicting with the dominant energy condition (DEC). The imposition of the DEC gives selection rules on the types of collision allowed. When the hypersurfaces don’t carry matter, one gets a soliton-like configuration. Then, at the intersection one has a co-dimension 2 or higher membrane standing alone in AdS-vacuum spacetime without conical singularities. Another result is that if the number of intersecting hypersurfaces goes to infinity the limiting spacetime is free of curvature singularities if the intersection is put at the boundary of each AdS bulk.
Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction

In this paper, we propose a method for the dimensionality reduction (DR) of spectral-spatial features in hyperspectral images (HSIs), under the umbrella of multilinear algebra, i.e., the algebra of tensors. The proposed approach is a tensor extension of conventional supervised manifold-learning-based DR. In particular, we define a tensor organization scheme for representing a pixel's spectral-spatial feature and develop tensor discriminative locality alignment (TDLA) for removing redundant information for subsequent classification. The optimal solution of TDLA is obtained by alternately optimizing each mode of the input tensors. The methods are tested on three public real HSI data sets collected by hyperspectral digital imagery collection experiment, reflective optics system imaging spectrometer, and airborne visible/infrared imaging spectrometer. The classification results show significant improvements in classification accuracies while using a small number of features.
Energy Efficiency of Mobile Clients in Cloud Computing

Energy efficiency is a fundamental consideration for mobile devices. Cloud computing has the potential to save mobile client energy but the savings from offloading the computation need to exceed the energy cost of the additional communication. In this paper we provide an analysis of the critical factors affecting the energy consumption of mobile clients in cloud computing. Further, we present our measurements about the central characteristics of contemporary mobile handheld devices that define the basic balance between local and remote computing. We also describe a concrete example, which demonstrates energy savings. We show that the trade-offs are highly sensitive to the exact characteristics of the workload, data communication patterns and technologies used, and discuss the implications for the design and engineering of energy efficient mobile cloud computing solutions.
Neurodevelopmental Outcomes in Infants with Hypoplastic Left Heart Syndrome after Hybrid Stage I Palliation

ii Dedication . iv Acknowledgments.v Vita . vii List of Tables . xvi List of Figures . xvii Chapter 1: Introduction and overview . 1 Chapter 2: Hypoplastic left heart syndrome: Update 2012 . 5 2.1 Cardiac anatomy . 5 2.2 Cerebral anatomy: the operculum . 7 2.3 Genetic and prenatal factors . 8 2.4 Options for surgical Stage I palliation . 13 2.5 Norwood procedure . 13 2.6 Norwood-Sano procedure . 15 2.7 Hybrid Stage I procedure . 17 2.5 Operative, post-operative, and interstage factors . 19 2.6 Stage II palliation . 22 2.7 Stage III Fontan completion.23 xv 2.6 Outcomes and neurodevelopment . 23 2.7 Conclusions . 31 Chapter 3: Neurodevelopment following Hybrid Stage I for HLHS . 33 3.1 Background . 34 3.2 Methods.41 3.3 Results . 48 3.4 Discussion . 52 3.5 Conclusions . 55 Chapter 4: Assessment of cerebral blood flow in HLHS after Hybrid Stage I palliation . 57 4.1 Background . 57 4.2 Methods.61 4.3 Results . 66 4.4 Discussion . 68 4.5 Limitations . 70 4.6 Conclusions . 72 References . 73 Appendix A : List of Abbreviations.91
Global Competition, Fee Income and Interest Rate Margins of Banks

The major thrust and novel feature of our study is the search for and the evidence we provide regarding the impact of global competition on banks' interest rate margins and profitability. We base our estimation approach on the dealership models of Stoll (1978), Ho and Stoll (1980) and Ho and Saunders (1981). Subsequent studies with a narrow focus on country-restricted monopoly measures (Saunders and Schumacher, 2000) in banking market or on foreign bank entry features (Claessens et al., 2001) ignore the spill-over effects of competition in global financial markets on domestic banking operations. We provide an avenue for competition in global financial markets to exert its influence on the operations of domestic banks in their respective countries by including the ratio of total foreign assets and liabilities to GDP in our model. This variable portrays the openness of the domestic financial system to global competition. Our estimation results suggest that exposure to global competition tends to narrow net interest rate margins and to reduce the rate of return on assets. We also find strong evidence for the expected inverse impact of the fee-to-income ratio on the interest rate margin and on profitability. The fee-to-interest-income ratio serves as a summary measure of competition in wholesale markets, as a reflection of advanced product mix, and as a variable which mirrors diversification benefits. Furthermore, a novel risk measure (volatility of gross income) impacts positively on the return on assets after tax as well as on the net interest margin, regardless of the model specification or the applied estimation techniques while costs show a negative relation to bank performance measures. JEL Classification: G21, L11
Social networks and sexual risk factor differences between cisgender heterosexual and cisgender LGBQ homeless youths

ABSTRACT Cisgender lesbian, gay, bisexual, and queer/questioning (LGBQ) homeless youths are more likely to engage in sexual risk behaviors relative to cisgender heterosexual homeless youths. Homeless youths (N = 1,046) were recruited and surveyed from three drop-in centers in Los Angeles, California. This study assessed differences in demographics, background experiences, and perceived social network norms in order to understand the disparities in rates of unprotected sex and concurrent sex between these two subgroups. Results indicate significant differences in engagement in risk factors as well as social network norms. Findings provide support for tailored sexual risk-reduction interventions that cater specifically to LGBQ homeless youths.
DEAR: Delay-bounded Energy-constrained Adaptive Routing in wireless sensor networks

Reliability and energy efficiency are critical issues in wireless sensor networks. In this work, we study Delay-bounded Energy-constrained Adaptive Routing (DEAR) problem with reliability, differential delay, and transmission energy consumption constraints in wireless sensor networks. We aim to route the connections in a manner such that link failure does not shut down the entire stream but allows a continuing flow for a significant portion of the traffic along multiple paths. This flexibility enabled by a multi-path routing scheme has the tradeoff of differential delay among the different paths. This requires increased memory in the base station to buffer the traffic until the data arrives on all the paths. Therefore, differential delay between the multiple paths should be bounded in a range to reduce additional hardware cost in the base station. Moreover, the energy consumption constraint should also be satisfied when transmitting packets among multiple paths. We present a pseudo-polynomial time solution to solve a special case of DEAR, representing edge delays as integers. Next, an (1+α)-approximation algorithm is proposed to solve the optimization version of the DEAR problem. An efficient heuristic is provided for the DEAR problem. We present numerical results confirming the advantage of our schemes as the first solution for the DEAR problem.
A Hybrid Neural Model for Type Classification of Entity Mentions

The semantic class (i.e., type) of an entity plays a vital role in many natural language processing tasks, such as question answering. However, most of existing type classification systems extensively rely on hand-crafted features. This paper introduces a hybrid neural model which classifies entity mentions to a wide-coverage set of 22 types derived from DBpedia. It consists of two parts. The mention model uses recurrent neural networks to recursively obtain the vector representation of an entity mention from the words it contains. The context model, on the other hand, employs multilayer perceptrons to obtain the hidden representation for contextual information of a mention. Representations obtained by the two parts are used together to predict the type distribution. Using automatically generated data, these two parts are jointly learned. Experimental studies illustrate that the proposed approach outperforms baseline methods. Moreover, when type information provided by our method is used in a question answering system, we observe a 14.7% relative improvement for the top-1 accuracy of answers.
Modeling and Learning User Preferences Over Sets

Although there has been signiﬁcant research on modeling and learning user preferences for various types of objects, there has been relatively little work on the problem of representing and learning preferences over sets of objects. We introduce a representation language, DD - PREF , that balances preferences for particular objects with preferences about the properties of the set. Speciﬁ-cally, we focus on the depth of objects (i.e., preferences for speciﬁc attribute values over others) and on the diversity of sets (i.e., preferences for broad vs. narrow distributions of attribute values). The DD - PREF framework is general and can incorporate additional object-and set-based preferences. We describe a greedy algorithm, DD-Select, for selecting satisfying sets from a collection of new objects, given a preference in this language. We show how preferences represented in DD - PREF can be learned from training data. Experimental results are given for three domains: a blocks world domain with several different task-based preferences; a real-world music playlist collection; and rover image data gathered in desert training exercises.
Lower bounds for the number of false null hypotheses for multiple testing of associations under general dependence structures

We propose probabilistic lower bounds for the number of false null hypotheses when testing multiple hypotheses of association simultaneously. The bounds are valid under general and unknown dependence structures between the test statistics. The power of the proposed estimator to detect the full proportion of false null hypotheses is discussed and compared to other estimators. The proposed estimator is shown to deliver a tight probabilistic lower bound for the number of false null hypotheses in a multiple testing situation even under strong dependence between test statistics. Copyright 2005, Oxford University Press.
Lie for a Dime

The Internet has enabled recruitment of large samples with specific characteristics. However, when researchers rely on participant self-report to determine eligibility, data quality depends on participant honesty. Across four studies on Amazon Mechanical Turk, we show that a substantial number of participants misrepresent theoretically relevant characteristics (e.g., demographics, product ownership) to meet eligibility criteria explicit in the studies, inferred by a previous exclusion from the study or inferred in previous experiences with similar studies. When recruiting rare populations, a large proportion of responses can be impostors. We provide recommendations about how to ensure that ineligible participants are excluded that are applicable to a wide variety of data collection efforts, which rely on self-report.
Restructuring Reward Mechanisms in Nicotine Addiction: A Pilot fMRI Study of Mindfulness-Oriented Recovery Enhancement for Cigarette Smokers

The primary goal of this pilot feasibility study was to examine the effects of Mindfulness-Oriented Recovery Enhancement (MORE), a behavioral treatment grounded in dual-process models derived from cognitive science, on frontostriatal reward processes among cigarette smokers. Healthy adult (N = 13; mean (SD) age 49 ± 12.2) smokers provided informed consent to participate in a 10-week study testing MORE versus a comparison group (CG). All participants underwent two fMRI scans: pre-tx and after 8-weeks of MORE. Emotion regulation (ER), smoking cue reactivity (CR), and resting-state functional connectivity (rsFC) were assessed at each fMRI visit; smoking and mood were assessed throughout. As compared to the CG, MORE significantly reduced smoking (d = 2.06) and increased positive affect (d = 2.02). MORE participants evidenced decreased CR-BOLD response in ventral striatum (VS; d = 1.57) and ventral prefrontal cortex (vPFC; d = 1.7) and increased positive ER-BOLD in VS (dVS = 2.13) and vPFC (dvmPFC = 2.66). Importantly, ER was correlated with smoking reduction (r's = .68 to .91) and increased positive affect (r's = .52 to .61). These findings provide preliminary evidence that MORE may facilitate the restructuring of reward processes and play a role in treating the pathophysiology of nicotine addiction.
Using backpropagation neural networks for flood forecasting in PhraNakhon Si Ayutthaya, Thailand

This research created models and an application for predicting the water levels at the gauging stations C.35 located at the Chao Phraya River, PhraNakhon Si Ayutthaya, Thailand by using backpropagation neural networks. The ranges of forecasting are one day, two days, and three days in advance. Rainfall, the water levels, and the water flowing rates in the Chao Phraya River measured from the gauging stations C.2, C.13, C.35, C.36, and C.37 collected in the year 2008-2010 were used for developing the water-level forecasting models. All created models were validated in term of mean square error (MSE), correlation coefficient, and tested in term of model efficiency index and MSE. The best model produced 90.1218% of accuracy and was employed in our flood warning application.
Pacific Asia Conference on Information Systems ( PACIS ) July 2008 USING E-BALANCED SCORECARD IN MANAGING THE PERFORMANCE AND EXCELLENCE OF ACADEMICIANS

The corporate performance and measurement discipline is dynamically changing. Organizations will increasingly identify sets of performance issues and build tracking mechanisms to monitor how the enterprise is doing. As a practice, performance measurement and management requires objectivity and careful planning in order to see its successful, continuous implementation. University Malaya is currently looking at the balanced scorecard (BSC) as the performance measurement and management tool in assessing the whole spectrum of what defines an excellent organization. This study specifically researches the use of an electronic BSC system (e-BSC) in measuring the performance and excellence of academicians in University Malaya from the perspectives of Financial, Customer, Internal Business Process (IBP) and Learning and Growth (L&G). To substantiate this research work, a survey and several interviews were conducted. Graphs are presented to depict the user requirements of the development of the e-BSC System. Furthermore, based on the obtained information too, the performance measurement framework is proposed. The advantage of adopting an automated balanced scorecard to manage the performance of academicians is that it sanctions excellence and provides a platform for better organization-wide alignment of strategies. The demand for a better performance measurement system has thus far been proven through the findings obtained.
Submodular Maximization over Sliding Windows

In this paper we study the extraction of representative elements in the data stream model in the form of submodular maximization. Different from the previous work on streaming submodular maximization, we are interested only in the recent data, and study the maximization problem over sliding windows. We provide a general reduction from the sliding window model to the standard streaming model, and thus our approach works for general constraints as long as there is a corresponding streaming algorithm in the standard streaming model. As a consequence, we obtain the first algorithms in the sliding window model for maximizing a monotone/non-monotone submodular function under cardinality and matroid constraints. We also propose several heuristics and show their efficiency in real-world datasets.