Miracle-dz/UniFAR-FTUs huggingface dataset:
Pacific Asia Conference on Information Systems ( PACIS ) 2014 EMPIRICAL STUDY OF THE MOTIVATIONS OF E-WOM SPREADING ON ONLINE FEEDBACK SYSTEM IN CHINA

Based on Social Exchange Theory and Motivation Crowding Theory, we study the motivations of consumers to spread e-WOM by benefit factors and cost factors. Meanwhile, we brought the motivation crowding effect into the analysis model, and discussed the effect of extrinsic motivation on intrinsic motivation and internalized extrinsic motivation. Through empirical analysis in China, we find that altruism, product involvement, self enhancement have significant positive influence on the intention to spread e-WOM, while execution cost has a significant negative influence on the intention, and economic incentive has significant crowding in effect on altruism, product involvement and self enhancement.
Novel mapping of a linear QR architecture

This paper presents a novel architecture mapping technique which was essential in the design of a QR array which forms the core processor of a single chip adaptive beamforming system. The mapping technique assigns a QR triangular array of 2m/sup 2/+3m+1 cells down onto a linear architecture of m+1 processors. The mapping results in a linear systolic architecture with one hundred percent hardware utilisation, local interconnects and individual processors for boundary and internal cell operations. In addition, this paper highlights the effect latency has on the validity of the linear architecture.
Building Ontologies: Towards a Unified Methodology

The use and importance of ontologies is becoming more widespread, however building ontologies is largely a black art. The aim of this paper is to identify and characterise what we currently know and to move towards the longer term goal of developing a comprehensive uni(cid:12)ed methodology. We (cid:12)rst identify dimensions for characterising ontologies, to be used as a basis for noting which techniques and guidelines for building ontologies apply in di(cid:11)erent circumstances. We then give an overview of the current state of the art, noting that most work addresses just a small part of the life cycle. The very few more complete methods are limited to case studies involving single ontologies and they are hard to compare. In the main part of this paper, we examine two such methods and give a framework for comparing and unifying them. We emphasise that di(cid:11)erent approaches are required for di(cid:11)erence circumstances, and give some guidelines for when to use which techniques. We conclude by considering how to further advance our understanding of building ontologies.
Eupafolin nanoparticles protect HaCaT keratinocytes from particulate matter-induced inflammation and oxidative stress

Exposure to particulate matter (PM), a major form of air pollution, can induce oxidative stress and inflammation and may lead to many diseases in various organ systems including the skin. Eupafolin, a flavonoid compound derived from Phyla nodiflora, has been previously shown to exhibit various pharmacological activities, including antioxidant and anti-inflammatory effects. Unfortunately, eupafolin is characterized by poor water solubility and skin penetration, which limits its clinical applications. To address these issues, we successfully synthesized a eupafolin nanoparticle delivery system (ENDS). Our findings showed that ENDS could overcome the physicochemical drawbacks of raw eupafolin with respect to water solubility and skin penetration, through reduction of particle size and formation of an amorphous state with hydrogen bonding. Moreover, ENDS was superior to raw eupafolin in attenuating PM-induced oxidative stress and inflammation in HaCaT keratinocytes, by mediating the antioxidant pathway (decreased reactive oxygen species production and nicotinamide adenine dinucleotide phosphate oxidase activity) and anti-inflammation pathway (decreased cyclooxygenase-2 expression and prostaglandin E2 production through downregulation of mitogen-activated protein kinase and nuclear factor-κB signaling). In summary, ENDS shows better antioxidant and anti-inflammatory activities than raw eupafolin through improvement of water solubility and skin penetration. Therefore, ENDS may potentially be used as a medicinal drug and/or cosmeceutical product to prevent PM-induced skin inflammation.
Aspect Selectors , Scales and Contextual Operators : An Analysis of by Temporal Adjuncts 1

Many temporal adjuncts select for specific aspectual classes; these adjuncts include measure adverbials like for an hour and interval adverbials like in an hour. While such adjuncts have traditionally served as diagnostics of telicity, it is only relatively recently that aspectual theorists have elucidated the relationship between the scalar-semantic meanings of these adjuncts and the internal structure of the event representations to which they apply (see Dowty 1979, Herweg 1991, Krifka 1998, inter alia). Krifka (1998) proposes that both measure adverbials and interval adverbials operate on representations that involve motion along a path. It seems entirely plausible that these adverbials should have path-based meanings, since they concern the ‘run times’ of processes. It is less clear whether path schemas can be applied to the semantics of aspectually sensitive temporal adverbs in general, and particularly those that denote time points. One such adverbial is the by time adverbial (BTA), which will be the focus of our attention in this paper. An example of the BTA is given in (1):
Biased Signaling in Naturally Occurring Mutations in Human Melanocortin-3 Receptor Gene

The melanocortin-3 receptor (MC3R) is primarily expressed in the hypothalamus and plays an important role in the regulation of energy homeostasis. Recently, some studies demonstrated that MC3R also signals through mitogen-activated protein kinases (MAPKs), especially extracellular signal-regulated kinases 1 and 2 (ERK1/2). ERK1/2 signaling is known to alter gene expression, potentially contributing to the prolonged action of melanocortins on energy homeostasis regulation. In the present study, we performed detailed functional studies on 8 novel naturally occurring MC3R mutations recently reported, and the effects of endogenous MC3R agonist, α-melanocyte stimulating hormone (MSH), on ERK1/2 signaling on all 22 naturally occurring MC3R mutations reported to date. We found that mutants D158Y and L299V were potential pathogenic causes to obesity. Four residues, F82, D158, L249 and L299, played critical roles in different aspects of MC3R function. α-MSH exhibited balanced activity in Gs-cAMP and ERK1/2 signaling pathways in 15 of the 22 mutant MC3Rs. The other 7 mutant MC3Rs were biased to either one of the signaling pathways. In summary, we provided novel data about the structure-function relationship of MC3R, identifying residues important for receptor function. We also demonstrated that some mutations exhibited biased signaling, preferentially activating one intracellular signaling pathway, adding a new layer of complexity to MC3R pharmacology.
Using an adaptive VAR Model for motion prediction in 3D hand tracking

A robust VAR-based (vector autoregressive) model is introduced for motion prediction in 3D hand tracking. This dynamic VAR motion model is learned in an online manner. The kinematic structure of the hand is accounted for in the form of constraints when solving for the parameters of the VAR model. Also integrated into the motion prediction model are adaptive weights that are optimised according to the reliability of past predictions. Experiments on synthetic and real video sequences show a substantial improvement in tracking performance when the robust VAR motion model is used. In fact, utilising the robust VAR model allows the tracker to handle fast out-of-plane hand movement with severe self-occlusion.
Mining probabilistic rules using nonmonotonic rule layers

This paper proposes a new framework for rule induction methods based on rule layers constrained by inequalities of accuracy and coverage. When the changes of accuracy and coverage are considered with an additional example, four patterns of updates of accuracy and coverage are observed and give two important inequalities of accuracy and coverage for induction of probabilistic rules. By using these two inequalities, the proposed method classifies a set of formulae into four layers: the rule layer, subrule layer (in and out) and the non-rule layer. Using these layers, updates of probabilistic rules are equivalent to their movement between layers. The proposed method was evaluated on datasets regarding headaches and meningitis, and the results show that the proposed method outperforms the conventional methods.
Preserving privacy of moving objects via temporal clustering of spatio-temporal data streams

The collection and exploration of a profusion of location data for mobility pattern mining has been met with stiff resistance from individuals and privacy groups due to privacy and security concerns. Such concerns have driven research to find the best trade-off between data mining and privacy. Unlike other techniques that make use of the spatial properties of spatio-temporal data to attain k-Anonymity, this study is inclined at exploring the possibilities of enforcing k-Anonymity by adroitly using the temporal domain of a spatio-temporal data. We propose a novel technique to achieve k-Anonymity for moving objects through temporal clustering. Our approach first perturbs and delays a trajectory trace once it arrives at a trusted server. This delay translates into the periodic accumulation of traces. Based on a system specified threshold distance and some newly defined time notions, redundant delayed traces are pruned and then similar traces are clustered. Traces in the same clusters are assigned the same spatial and time value, thus making them quasi-identifiers of the k-Anonymity privacy paradigm. We illustrate through empirical experiments the effectiveness of our technique on a real and synthetic dataset; and show that our approach provides strong privacy through k-Anonymity.
CS 294 – Practical Machine Learning Term Project Robust Classification for Data with Interval Uncertainty and Label Errors

A robust linear binary classification problem will be considered. Robustness will be for data with interval uncertainty, i.e., data points are unknown but their mean and bounds on their components are known. Convex optimization formulation for the problem is derived and the method is applied to a genomic micro-array data. An extension for this framework will be developed for data with uncertainties due to label errors. For this problem, the convex optimization formulation is derived. The implementation of this method is postponed due to lack of a useful data set.
An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions

Remote sensing technology plays an important role in monitoring rapid changes of the Earth's surface. However, sensors that can simultaneously provide satellite images with both high temporal and spatial resolution haven’t been designed yet. This paper proposes an improved spatial and temporal adaptive reflectance fusion model (STARFM) with the help of an Unmixing-based method (USTARFM) to generate the high spatial and temporal data needed for the study of heterogeneous areas. The results showed that the USTARFM had higher accuracy than STARFM methods in two aspects of analysis: individual bands and of heterogeneity analysis. Taking the predicted NIR band as an example, the correlation coefficients (r) for the USTARFM, STARFM and unmixing methods were 0.96, 0.95, 0.90, respectively (p-value < 0.001); Root Mean Square Error (RMSE) values were 0.0245, 0.0300, 0.0401, respectively; and ERGAS values were 0.5416, 0.6507, 0.8737, respectively. The USTARM showed consistently higher performance than STARM when the degree of heterogeneity ranged from 2 to 10, highlighting that the use of this method provides the capacity to solve the data fusion problems faced when using STARFM. Additionally, the USTARFM method could help researchers achieve better performance than STARFM at a smaller window size from its heterogeneous land surface quantitative representation.
Liposome armed with herpes virus-derived gH625 peptide to overcome doxorubicin resistance in lung adenocarcinoma cell lines

New delivery systems including liposomes have been developed to circumvent drug resistance. To enhance the antitumor efficacy of liposomes encapsulating anti-cancer agents, we used liposomes externally conjugated to the 20 residue peptide gH625. Physicochemical characterization of the liposome system showed a size of 140 nm with uniform distribution and high doxorubicin encapsulation efficiency. We evaluated the effects of increasing concentrations of liposomes encapsulating Doxo (LipoDoxo), liposomes encapsulating Doxo conjugated to gH625 (LipoDoxo-gH625), empty liposomes (Lipo) or free Doxo on growth inhibition of either wild type (A549) or doxorubicin-resistant (A549 Dx) human lung adenocarcinoma. After 72 h, we found that the growth inhibition induced by LipoDoxo-gH625 was higher than that caused by LipoDoxo with an IC50 of 1 and 0.3 μM in A549 and A549 Dx cells, respectively. The data on cell growth inhibition were paralleled by an higher oxidative stress and an increased uptake of Doxo induced by LipoDoxo-gH625 compared to LipoDoxo, above all in A549 Dx cells. Cytometric analysis showed that the antiproliferative effects of each drug treatment were mainly due to the induction of apoptosis. In conclusion, liposomes armed with gH625 are able to overcome doxorubicin resistance in lung adenocarcinoma cell lines.
DLFR: Taming stochastic behavior of Global Networks.

Summary Mobile ad hoc networks (MANET) are difficult to support real time services efficiently because of their unpredictable and frequent topology changes. One of the important concerns is the reliability of routes. Although finding optimal path for MANET is incompletely defined problem and specific routing protocols trade off efficiency for the reliability. In particular, typical shortest path estimating routing protocols use redundant paths to achieve reliability. In this paper, we propose a new routing protocol for estimating the shortest path in such open networks by introducing a statistical metric for “the reliable routing path selection”.
An Overview on Semantical Constraints for Database Models

Modeling of semantics is one of the most diicult tasks in database design. Constraints are used to express database semantics. They are used diierently in database models. They express domain restrictions, specify relationships between components and state database behavior. The utilization depends on the richness of the type system used in the model. The relational model is using a simple type system and has a very large set of integrity constraints. Semantical models are using richer type systems which express also diierent types of integrity constraints. At the same time, the theory of integrity constraints is more complex. Object-oriented models use either a simple type system or type systems like the semantical models. The theory of integrity constraints is still under development. This overview tries to give a unifying framework on integrity constraints.
Finding the Number of Clusters in Unlabelled Datasets Using Extended Cluster Count Extraction ( ECCE )

Clustering analysis is the task of partitioning a set of objects O = {O1. On} into C self-similar subsets based on available data. In general, clustering of unlabeled data poses three major problems: 1) Assessing cluster tendency, i.e., how many clusters to seek? 2) Partitioning the data into C meaningful groups, and 3) Validating the c clusters that are discovered. All clustering algorithms ultimately rely on one or more human inputs, and the most important input is number of clusters (C) to seek. There are many pre and post clustering methods which relieves the user from this choice. These methods ultimately make the choice by thresholding some value in the code. Thus, the choice of c is transferred to the equivalent choice of the hidden threshold that determines C "automatically". In contrast, tendency assessment attempts to estimate c before clustering occurs. Here, we represent the structure of the unlabeled data sets as a Reordered Dissimilarity Image (RDI) where pair wise dissimilarity information about a data set including ‘n’ objects is represented as n x n image. RDI is generated using VAT (Visual Assessment of Cluster tendency), which highlights potential clusters as a set of “dark blocks” along the diagonal of the image, so that number of clusters can be easily estimated using the number of dark blocks across the diagonal. We develop a new method called “Extended Cluster Count Extraction (ECCE) for counting the number of clusters formed along the diagonal of the RDI. General Terms: Data Mining, Image Processing, Artificial Intelligence.
Inflation and Economic Growth

The existence and nature of a link between inflation and economic growth has been the subject of considerable interest and debate. High and sustainable economic growth and low inflation are two of the main objectives of macroeconomic policy. Hence, if high inflation is harmful for an economy and low inflation is beneficial, then it is reasonable to ask, what is the optimal level of inflation for an economy? In general, is there any link between inflation and economic growth? In this thesis, we estimate the relationship between inflation and economic growth by studying their co-integrated relationship using the error correction model. After estimating this link, we turn to estimate the threshold level of inflation. The results indicate that there exist a positive long-run relationship between inflation and economic growth in Finland. In addition, by assuming that such a relationship is non-linear, we find that the Finnish economy grows at its highest rate when inflation is at 4 percent.
Google Scholar: The New Generation of Citation Indexes

Google Scholar (http://scholar.google.com) provides a new method of locating potentially relevant articles on a given subject by identifying subsequent articles that cite a previously published article. An important feature of Google Scholar is that researchers can use it to trace interconnections among authors citing articles on the same topic and to determine the frequency with which others cite a specific article, as it has a “cited by” feature. This study begins with an overview of how to use Google Scholar for citation analysis and identifies advanced search techniques not well documented by Google Scholar. This study also compares the citation counts provided by Web of Science and Google Scholar for articles in the field of “Webometrics. ” It makes several suggestions for improving Google Scholar. Finally, it concludes that Google Scholar provides a free alternative or complement to other citation indexes.
Employment, hours of work and the optimal design of earned income tax credits

This paper examines the optimal schedule of marginal tax rates and the design of earned income tax credits. The analysis is based on a structural labour supply model which incorporates unobserved heterogeneity, fixed costs of work and the detailed non-convexities of the tax and welfare system. An analytical framework is developed that allows explicitly for an extensive margin in work choices and also the partial observability of hours of work. This is contrasted to the standard case in which only earnings (and non-labour income) are observable to the government. The empirical motivation is the earned income tax credit reforms in Britain which include a minimum hours requirement at 16 hours per week and a further bonus at 30 hours. Our analysis examines the case for the use of hours-contingent payments and lends support for the overall structure of the tax credit reforms. However, we also provide a strong case for a further reduction of marginal rates for lower earners but only those with school age children.
Dynamics of Tubulovesicular Recycling Endosomes in Hippocampal Neurons

Neurons are polarized cells, the activity of which relies on the morphological and functional differences between their axonal and somatodendritic domains. One mechanism for establishing and maintaining neuronal polarity is via the selective targeting of proteins to these domains. The endocytic pathway plays a major role in the generation and maintenance of cellular polarity by selectively sorting and recycling endocytosed plasma membrane proteins. In this study we first show that endogenous syntaxin 13 localizes to tubulovesicular organelles that are present in the somatodendritic and axonal domains of neurons. These organelles contain and actively recycle transferrin receptor and are sensitive to brefeldin A, suggesting that they are analogous to the tubulovesicular recycling endosomes in non-neuronal cells. We next use a syntaxin 13–GFP fusion protein transiently expressed in hippocampal neurons, together with time-lapse microscopy, to study the dynamics of the endosomal system in neurons. The analysis revealed the presence of two distinct classes of syntaxin 13-labeled endosomes: round–oval stationary organelles and highly mobile tubulovesicular structures. The dynamic population of tubulovesicular endosomes travels in both directions along microtubules in dendrites and axons. The mobile organelles appear to fuse with and bud from the stationary endosomes, possibly as a means of delivering and picking up their cargo.
A LMI solution to the LQ problem for discrete-time singularly perturbed systems

In this article, an alternative LMI solution for the linear quadratic optimal control design is proposed for discrete-time linear systems in the singular perturbation form. This approach is particularly adapted for the case of high dimension systems. An extension to polytopic uncertain system and an example of practical application to the robust steering control of hot strip mill are also presented.
Global Optimization of Clusters in Gene Expression Data of DNA Microarrays by Deterministic Annealing

The analysis of DNA microarry data is one of the most important things for functional genomics research. The matrix representation of microarray data and its successive roptimalsincisional hyperplanes is a useful platform for developing optimization algorithms to determine the optimal partitioning of pairwise proximity matrix representing completely connected and weighted graph. We developed Deterministic Annealing (DA) approach to determine the successive optimal binary partitioning. DA algorithm demon- strated good performance with the ability to find the rglobally optimalsbinary partitions. In addition, the objects that have not been clustered at small non- zero temperature, are considered to be very sensitive to even small randomness, and can be used to estimate the reliability of the clustering.
K-Anonymous Cloaking Algorithm Based on Weighted Adjacency Graph for Preserving Location Privacy

The propagation of position identifying devices, such as GPS (Global Positioning System), becomes increasingly a privacy threat in location-based services (LBSs). However, in order to enjoy such services, the user must precisely disclose his/her exact location to the LBS. So, it is a key challenge to efficiently preserve user's privacy while accessing LBS. For this, the existing method employs a 2PASS cloaking framework that not only hides the actual user location but also reduces bandwidth consumption. However, it suffers from privacy attack. Therefore, we aim to provide the solutions which can preserve user privacy by utilizing k-anonymity mechanism. In this paper, we propose a weighted adjacency graph based k-anonymous cloaking technique that can provide protection to user and also reduce bandwidth usages. Our cloaking approach efficiently supports k-nearest neighbor queries without revealing private information of the query initiator. We demonstrate via experimental results that our algorithm yields much better performance than the existing one.