Miracle-dz/UniFAR-FTUs huggingface dataset:
Managing Advertising and Promotion for Long-Run Profitability

In recent years, manufacturers have become increasingly disposed toward the use of sales promotions, often at the cost of advertising. Yet the long-term implications of these changes for brand profitability remain unclear. In this paper, we seek to offer insights into this important issue. We consider the questions of i) whether it is more desirable to ad-vertise or promote, ii) whether it is better to use frequent, shallow promotions or infrequent, deep promotions, and iii) how changes in regular prices affect sales relative to increases in price promotions. Additional insights regarding brand equity, the relative magnitude of short- and long-term effects, and the decomposition of advertising and promotion elasticities across choice and quantity decisions are obtained. To address these points, we develop a heteroscedastic, varying-parameter joint probit choice and regression quantity model. Our approach allows consumers' responses to short-term marketing activities to change in response to changes in marketing actions over the long term. We also accommodate the possibility of competitive reactions to policy changes of a brand. The model is estimated for a consumer packaged good category by using over eight years of panel data. The resulting parameters enable us to assess the effects of changes in advertising and promotion policies on sales and profits. Our results show that, in the long term, advertising has a positive effect on "brand equity" while promotions have a negative effect. Furthermore, we find price promotion elasticities to be larger than regular price elasticities in the short term, but smaller than regular price elasticities when long-term effects are considered. Consistent with previous research, we also find that most of the effect of a price cut is manifested in consumers' brand choice decisions in the short term, but when long-term effects are again considered, this result no longer holds. Last, we estimate that the long-term effects of promotions on sales are negative overall, and about two-fifths the magnitude of the positive short-term effects. Finally, making reasonable cost and margin assumptions, we conduct simulations to assess the relative profit impact of long-term changes in pricing, advertising, or promotion policies. Our results show regular price decreases to have a generally negative effect on the long-term profits of brands, advertising to be profitable for two of the brands, and increases in price promotions to be uniformly unprofitable.
Bridging the Domain Shift by Domain Adaptive Dictionary Learning

Domain adaptation (DA) tackles the problem where data from the training set (source domain) and test set (target domain) have different underlying distributions. For instance, training and testing images may be acquired under different environments, viewpoints and illumination conditions. In this paper, we focus on the more challenging unsupervised DA problem where the samples in the target domain are unlabeled. It is noticed that dictionary learning has gained a lot of popularity due to the fact that images of interest could be reconstructed sparsely in an appropriately learned dictionary [1]. Specifically, we propose a novel domain-adaptive dictionary learning approach to generate a set of intermediate domains which bridge the gap between source and target domains. Our approach defines two types of dictionaries: a common dictionary and a domain-specific dictionary. The overall learning process illustrated in Figure 1 consists of three steps: (1) At the beginning, we first learn the common dictionary DC, domainspecific dictionaries D0 and Dt for source and target domains. (2) At the k-th step, we enforce the recovered feature representations of target data in all available domains to have the same sparse codes, while adapting the most recently obtained dictionary Dk to better represent the target domain. Then we multiply dictionaries in the k-th domain with the corresponding sparse codes to recover feature representations of target data Xk t in this domain. (3) We update Dk to find the next domain-specific dictionary Dk+1 by further minimizing the reconstruction error in representing the target data. Then we alternate between the steps of sparse coding and dictionary updating until the stopping criteria is satisfied. Notations: Let X s ∈ Rd×Ns , X t ∈ Rd×Nt be the feature representations of source and target data respectively, where d is the feature dimension, Ns and Nt are the number of samples in the two domains. The feature representations of recovered source and target data in the k-th intermediate domain are denoted as Xs ∈ Rd×Ns and Xk t ∈ Rd×Nt respectively. The common dictionary is denoted as DC, whereas source-specific and targetspecific dictionaries are denoted as D0, Dt respectively. Similarly, we use Dk,k = 1.N to denote the domain-specific dictionary for the k-th domain, where N is the number of intermediate domains. We set all the dictionaries to be of the same size ∈Rd×n. At the beginning, we learn the common dictionary DC by minimizing the reconstruction error of both source and target data as follows:
Low-level multimodal integration on Riemannian manifolds for automatic pedestrian detection

In Computer Vision, automated pedestrian detection is surely one of the hottest topics, with important applications in surveillance and security. To this end, information integration from different imaging modalities, such as thermal infrared and visible spectrum, can significantly improve the detection rate with respect to monomodal strategies. A common scheme consists of extracting two sets of features, from thermal and visible images of the same scene respectively, and stacking them together into a single feature set, ignoring possible and meaningful inter-media dependencies. Here we propose a fusion scheme which acts at the feature-level, taking standard pixel characteristics (such as first/second order spatial derivatives or Local Binary Pattern) and designing a composite descriptor that, at the same time, encodes the information coming from the separate modalities, as well as the cross-modal mutual relationships in the form of covariances. The descriptor, which lies on a Riemannian manifold, is projected onto a Euclidean tangent space and then fed into a Support Vector Machine classifier. Experiments performed on the OTCBVS dataset [1], and validated statistically, demonstrate that our method outperforms significantly the single modality policies as well as different fusion schemes at the pixel, feature and decision level.
Structural analysis: From method and metaphor to theory and substance.

Structural (or network) analysis has mystified many social scientists. Some have rejected it as mere methodology, which lacks due regard for substantive issues. Some have fled from its unusual terms and techniques, not having played with blocks and graphs since grammar school. Some have dismissed one portion for the whole, saying, for example, that their study of class structure has little need for the focus on friendship ties emphasized in network analysis. And some have scorned it as nothing new, claiming that they also study "social structure. " Others have bolted on variables such as network "density" as they would a turbocharger in order to boost explained variance. Still others, attracted by the capability of studying nonhierarchical, nongroup structures, have expanded structural analysis into a network ideology that advocates egalitarian, open communities. Some have even used "network" as a verb and "networking" as a noun to advocate the deliberate creation and use of social networks for such desired ends as getting jobs or integrating communities. These misconceptions have arisen because too many analysts and
Development of Faceted and Synonym Search Support for the Ontology Application Management Framework

Semantic search is a form of search that goes beyond keyword-based searching. Searching based on keywords typically has several disadvantages including homonym and synonym problems which can reduce the retrieval effectiveness of a search system. Ontology-based search is a form of semantic search that can be applied to searching structured data, i.e. RDF data, which are exported from relational database. The Ontology Application Management (OAM) framework can provide support for ontology-based search application development over RDF data using an application template that generates queries based on SPARQL template. However, OAM still relies on keywordbased search when the properties are datatype properties, i.e. those having property values as literals. In this paper, we propose to use faceted and synonym search to augment the keyword-based search over datatype property values. One of the main goals is to provide a generic framework for improving the effectiveness of searching RDF data. Our system design adopted the service-oriented architecture (SOA) approach in creating reusable service components. Two key components are synonym and aggregation service Web APIs. The SPARQL query templates for implementing synonym and faceted search are described. Finally, we demonstrate an adoption of the framework in searching a large-scale database in the professional qualification domain.
Modeling and Tracing Stakeholders' Goals across Notations using RE-Tools

The ability to represent stakeholders' goals and their opera- tionalizations, through tasks, resources, system requirements and spec- ifications, helps better ensure that the projected system meets its in- tended goals. To offer such an ability, various notations have been de- veloped, but somewhat independently of each other, each for its own concepts and with its own tool. As a result, it is difficult to establish end-to-end traceability among the various concepts. This paper presents RE-Tools - a toolkit towards an integrated modeling of such concepts, us- ing i*, the NFR framework, KAOS and Problem Frames. RE-Tools uses a meta-model approach to representing and integrating the notations, which includes shared, generalized meta-classes and cross-notational goal achievement contribution links. This approach is intended to allow for a uniform application of the Label Propagation Procedure in determining goal achievement across the supported notations, using either the open or closed world assumption.
The modeling of partial discharge waveforms in power systems equipment

This paper looks at the modeling of partial discharge (PD) waveforms to gain an understanding of the characteristics of PD signals. To accurately model the PD waveforms, a high sampling rate of 500 MHz was used in collecting the PD data with the use of a high-frequency current transformer. While characteristics such as rise time, fall time and pulse width have been used in the past to analyze PD waveforms, this paper uses Prony's method to fit a digital transfer function to the PD waveform, such that the impulse response of the digital filter approximately matches the waveform. Results show that this technique can be applied to internal, corona and surface partial discharge signals, and a model like this can be used for simulating and interpreting PD waveforms.
Introduction Towards a new functional anatomy of language

The classical brain-language model derived from the work of Broca, Wernicke, Lichtheim, Geschwind, and others has been useful as a heuristic model that stimulates research and as a clinical model that guides diagnosis. However, it is now uncontroversial that the classical model is (i) empirically wrong in that it cannot account for the range of aphasic syndromes, (ii) linguistically underspecified to an extent that prohibits contact with the language sciences, and (iii) anatomically underspecified. We briefly summarize some of the central issues that motivate why a new functional anatomy of language is necessary, in the context of introducing a collection of articles that describe systematic new attempts at specifying the new functional anatomy. The major convergent observations are highlighted and the emergent conceptual and empirical trends are identified. q 2004 Elsevier B.V. All rights reserved.
Statistical analysis of emotions and opinions at Digg website

We performed statistical analysis on data from the Digg.com website, which enables its users to express their opinion on news stories by taking part in forum-like discussions as well as directly evaluate previous posts and stories by assigning so called "diggs". Owing to fact that the content of each post has been annotated with its emotional value, apart from the strictly structural properties, the study also includes an analysis of the average emotional response of the posts commenting the main story. While analysing correlations at the story level, an interesting relationship between the number of diggs and the number of comments received by a story was found. The correlation between the two quantities is high for data where small threads dominate and consistently decreases for longer threads. However, while the correlation of the number of diggs and the average emotional response tends to grow for longer threads, correlations between numbers of comments and the average emotional response are almost zero. We also show that the initial set of comments given to a story has a substantial impact on the further "life" of the discussion: high negative average emotions in the first 10 comments lead to longer threads while the opposite situation results in shorter discussions. We also suggest presence of two different mechanisms governing the evolution of the discussion and, consequently, its length.
The microtubule-affecting drug paclitaxel has antiangiogenic activity.

Endothelial cell migration is a critical event during angiogenesis, and inhibitors of cell motility can affect the angiogenic process. Paclitaxel (Taxol(R)), a microtubule-stabilizing antineoplastic cytotoxic drug, inhibits motility and invasiveness of several cell types. The aim of this study was to investigate the effect of paclitaxel on endothelial cell functions and on angiogenesis. In vivo, paclitaxel (20-28 mg/kg i.v.) significantly inhibited the angiogenic response induced by tumor cell supernatant embedded in a pellet of reconstituted basement membrane (Matrigel) injected s.c. into C57BL/6N mice. In vitro, paclitaxel inhibited endothelial cell proliferation, motility, invasiveness, and cord formation on Matrigel in a dose-dependent manner. The antiangiogenic activity of paclitaxel was not linked to its cytotoxicity, since inhibition of endothelial cell chemotaxis and invasiveness occurred at drug concentrations which did not affect endothelial cell proliferation. Another cytotoxic drug, cisplatin, that inhibited endothelial cell proliferation in vitro, did not affect angiogenesis in vivo. These data indicate that paclitaxel has a strong antiangiogenic activity, a property that might contribute to its antineoplastic activity in vivo.
Leveraging Social Networks for Increased BitTorrent Robustness

In peer-to-peer content delivery systems, such as BitTorrent, there may exist nodes that are non-cooperative and do not contribute their upload bandwidth to the system while still downloading content from others. The current widely used countermeasures against this freeriding behavior have been shown to be ineffective. In this paper, we address the problem by leveraging the trust latent in the social networks and explicitly incorporating the social links as part of the BitTorrent content distribution infrastructure. Our extensive system evaluation produces several insights. First, the social network topology alone without the trackers is an efficient and scalable content distribution medium. Second, thanks to the cooperative social links, BitTorrent's robustness to freeriding significantly improves. Finally, we find that a hybrid solution in which peers download from both their friends and other peers obtained from the trackers has the highest robustness to freeriding, shortest download completion times and the most balanced upload bandwidth utilization.
DockFlow: Achieving interoperability of protein docking tools across heterogeneous Grid middleware

Enabling the seamless integration between applications executing on heterogeneous Grid middleware poses a number of challenges to both application scientists and middleware developers. We highlight some of these challenges in the context of designing and implementing DockFlow. DockFlow is a virtual screening environment integrating four Grid-based protein docking tools that execute on different Grid middleware technologies at different locations. We propose extensions that can be applied to any Grid-based workflow system to support the run-time interoperability between the available tools. The extensions are generic, and as an example we describe how they have been implemented in the InforSense workflow system. We also present experimental results that evaluate the tradeoffs between performance and usability of the proposed methods.
Local Rules for Global MAP: When Do They Work ?

We consider the question of computing Maximum A Posteriori (MAP) assignment in an arbitrary pair-wise Markov Random Field (MRF). We present a randomized iterative algorithm based on simple local updates. The algorithm, starting with an arbitrary initial assignment, updates it in each iteration by first, picking a random node, then selecting an (appropriately chosen) random local neighborhood and optimizing over this local neighborhood. Somewhat surprisingly, we show that this algorithm finds a near optimal assignment within n log2 n iterations with high probability for any n node pair-wise MRF with geometry (i.e. MRF graph with polynomial growth) with the approximation error depending on (in a reasonable manner) the geometric growth rate of the graph and the average radius of the local neighborhood - this allows for a graceful tradeoff between the complexity of the algorithm and the approximation error. Through extensive simulations, we show that our algorithm finds extremely good approximate solutions for various kinds of MRFs with geometry.
Antiinflammatory Activity of Cinnamon (Cinnamomum zeylanicum) Bark Essential Oil in a Human Skin Disease Model

The effect of cinnamon (Cinnamomum zeylanicum) bark essential oil (CBEO) on human skin cells has not been elucidated. Therefore, we investigated the activity of a commercially available CBEO in a validated human dermal fibroblast system, a model of chronic inflammation and fibrosis. We first evaluated the impact of CBEO on 17 protein biomarkers that play critical roles in inflammation and tissue remodeling. The impact of CBEO on genome‐wide gene expression was also evaluated. CBEO showed strong anti‐proliferative effects on skin cells and significantly inhibited the production of several inflammatory biomarkers, including vascular cell adhesion molecule‐1, intercellular cell adhesion molecule‐1, monocyte chemoattractant protein‐1, interferon gamma‐induced protein 10, interferon‐inducible T‐cell alpha chemoattractant, and monokine induced by gamma interferon. In addition, CBEO significantly inhibited the production of several tissue remodeling molecules, including epidermal growth factor receptor, matrix metalloproteinase‐1, and plasminogen activator inhibitor‐1. Macrophage colony‐stimulating factor, which is an immunomodulatory protein molecule, was also significantly inhibited by CBEO. Furthermore, CBEO significantly modulated global gene expression and altered signaling pathways, many of which are important in inflammation, tissue remodeling, and cancer biology. The study shows that CBEO is a promising antiinflammatory agent; however, further research is required to clarify its clinical efficacy. © 2017 The Authors. Phytotherapy Research published by John Wiley & Sons Ltd.
Testing Technique of BIST: A Survey

As the compactness of system-on-chip (SoC) increase, it becomes striking to integrate dedicated test logic on a chip. Starting with a broad idea of test problems, this survey paper focus on “Chip” Built in Self-Test (BIST) study and its promotion for board and system-level applications. This paper gives brief informative review of Built-in Self-test (BIST) and its testing techniques. Recently BIST Research is being highly used in VLSI and SoC testing for the detection fault coverage.
UAIC Participation at RTE-7

This paper describes the fifth participation of the UAIC textual entailment engine at the RTE shared task. Our approach is rule based and makes use of the notion of predicational semantics for detecting entailment. The system used is based on the system built for the RTE-6 challenge, which we have further modified and improved for the current task. The system works by attempting to match every entity in the hypothesis to at least one entity in the text, but for this version of the systems, the matching process is predication driven, which is to say we used predicates in T and H as pivot points for determining matching entities. Matching is achieved by using extensive semantic knowledge from suck knowledge bases as DIRT, VerbNet, WordNet, VerbOcean, Wikipedia and the Acronym database.
Web question answering through automatically learned patterns

While being successful in providing keyword based access to Web pages, commercial search portals, such as Google, Yahoo, AltaVista, and AOL, still lack the ability to answer questions expressed in a natural language. We explore the feasibility of a completely trainable approach to the automated question answering on the Web or large scale digital libraries. By using the inherent redundancy of large scale collections, each candidate answer found by the system is triangulated (confirmed or disconfirmed) against other possible answers. Since our approach is entirely self-learning and does not involve any linguistic resources it can be easily implemented within digital libraries or Web search portals.
Molecular Line Emission from Massive Protostellar Disks: Predictions for ALMA and EVLA

We compute the molecular line emission of massive protostellar disks by solving the equation of radiative transfer through the cores and disks produced by the recent radiation-hydrodynamic simulations of Krumholz, Klein, & McKee. We find that in several representative lines the disks show brightness temperatures of hundreds of kelvins over velocity channels ~10 km s-1 wide, extending over regions hundreds of AU in size. We process the computed intensities to model the performance of next-generation radio and submillimeter telescopes. Our calculations show that observations using facilities such as the Expanded Very Large Array (EVLA) and Atacama Large Millimeter Array (ALMA) should be able to detect massive protostellar disks and measure their rotation curves, at least in the nearest massive star-forming regions. They should also detect significant substructure and nonaxisymmetry in the disks, and in some cases may be able to detect star-disk velocity offsets of a few km s-1, both of which are the result of strong gravitational instability in massive disks. We use our simulations to explore the strengths and weaknesses of different observational techniques, and we also discuss how observations of massive protostellar disks may be used to distinguish between alternative models of massive star formation.