9,429 research outputs found

    Consumer demand for variety: intertemporal effects of consumption, product switching and pricing policies

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    The concept of diminishing marginal utility is a cornerstone of economic theory. The consumption of a good typically creates satiation that diminishes the marginal utility of consuming more. Temporal satiation induces consumers to increase their stimulation level by seeking variety and therefore substitute towards other goods (substitutability across time) or other differentiated versions (products) of the good (substitutability across products). The literature on variety-seeking has developed along two strands, each focusing on only one type of substitutability. I specify a demand model that attempts to link these two strands of the literature. This issue is economically relevant because both types of substitutability are important for retailers and manufacturers in designing intertemporal price discrimination strategies. The consumer demand model specified allows consumption to have an enduring effect and the marginal utility of the different products to vary over consumption occasions. Consumers are assumed to make rational purchase decisions by taking into account, not only current and future satiation levels, but also prices and product choices. I then use the model to evaluate the demand implications of a major pricing policy change from hi-low pricing to an everyday low pricing strategy. I find evidence that consumption has a lasting effect on utility that induces substitutability across time and that the median consumer has a taste for variety in her product decisions. Consumers are found to be forward-looking with respect to the duration since the last purchase, to price expectations and product choices. Pricing policy simulations suggest that retailers may increase revenue by reducing the variance of prices, but that lowering the everyday level of prices may be unprofitable.Variety seeking; Intertemporal effects of consumption; product switching; Pricing; Dynamic demand;

    Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations

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    Dialog act (DA) recognition is a task that has been widely explored over the years. Recently, most approaches to the task explored different DNN architectures to combine the representations of the words in a segment and generate a segment representation that provides cues for intention. In this study, we explore means to generate more informative segment representations, not only by exploring different network architectures, but also by considering different token representations, not only at the word level, but also at the character and functional levels. At the word level, in addition to the commonly used uncontextualized embeddings, we explore the use of contextualized representations, which provide information concerning word sense and segment structure. Character-level tokenization is important to capture intention-related morphological aspects that cannot be captured at the word level. Finally, the functional level provides an abstraction from words, which shifts the focus to the structure of the segment. We also explore approaches to enrich the segment representation with context information from the history of the dialog, both in terms of the classifications of the surrounding segments and the turn-taking history. This kind of information has already been proved important for the disambiguation of DAs in previous studies. Nevertheless, we are able to capture additional information by considering a summary of the dialog history and a wider turn-taking context. By combining the best approaches at each step, we achieve results that surpass the previous state-of-the-art on generic DA recognition on both SwDA and MRDA, two of the most widely explored corpora for the task. Furthermore, by considering both past and future context, simulating annotation scenario, our approach achieves a performance similar to that of a human annotator on SwDA and surpasses it on MRDA.Comment: 38 pages, 7 figures, 9 tables, submitted to JAI

    Periodic orbits 1-5 of quadratic polynomials on a new coordinate plane

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    While iterating the quadratic polynomial f_{c}(x)=x^{2}+c the degree of the iterates grows very rapidly, and therefore solving the equations corresponding to periodic orbits becomes very difficult even for periodic orbits with a low period. In this work we present a new iteration model by introducing a change of variables into an (u,v)-plane, which changes situation drastically. As an excellent example of this we can compare equations of orbits period four on (x,c)- and (u,v)-planes. In the latter case, this equation is of degree two with respect to u and it can be solved explicitly. In former case the corresponding equation ((((x^{2}+c)^{2}+c)^{2}+c)^{2}+c-x)/((x^{2}+c)^{2}+c-x)=0 is of degree 12 and it is thus much more difficult to solve

    CONVERSION to ORGANIC FARMING in MAINLAND PORTUGAL

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    The objectives of the research were: i) to assess the in-conversion period as a barrier impeding farms conversion to organic; ii) to assess the potential of conversion-grade markets in removing this barrier; iii) to identify other barriers (drives) along the food chain impeding (easing) farms conversion in mainland Portugal. Results show that the in-conversion period is not the major barrier to conversion nor is a good idea the set-up of conversion grade markets to help Portuguese farms’ conversion. Conversion feasibility depends of the organic market premium prices, in intensive farms, and of the CAP organic agri-environmental area payments, in extensive farms.organic farming, conversion, conversion grade markets, market premium prices, CAP payments.

    Using Generic Summarization to Improve Music Information Retrieval Tasks

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    In order to satisfy processing time constraints, many MIR tasks process only a segment of the whole music signal. This practice may lead to decreasing performance, since the most important information for the tasks may not be in those processed segments. In this paper, we leverage generic summarization algorithms, previously applied to text and speech summarization, to summarize items in music datasets. These algorithms build summaries, that are both concise and diverse, by selecting appropriate segments from the input signal which makes them good candidates to summarize music as well. We evaluate the summarization process on binary and multiclass music genre classification tasks, by comparing the performance obtained using summarized datasets against the performances obtained using continuous segments (which is the traditional method used for addressing the previously mentioned time constraints) and full songs of the same original dataset. We show that GRASSHOPPER, LexRank, LSA, MMR, and a Support Sets-based Centrality model improve classification performance when compared to selected 30-second baselines. We also show that summarized datasets lead to a classification performance whose difference is not statistically significant from using full songs. Furthermore, we make an argument stating the advantages of sharing summarized datasets for future MIR research.Comment: 24 pages, 10 tables; Submitted to IEEE/ACM Transactions on Audio, Speech and Language Processin

    On the Application of Generic Summarization Algorithms to Music

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    Several generic summarization algorithms were developed in the past and successfully applied in fields such as text and speech summarization. In this paper, we review and apply these algorithms to music. To evaluate this summarization's performance, we adopt an extrinsic approach: we compare a Fado Genre Classifier's performance using truncated contiguous clips against the summaries extracted with those algorithms on 2 different datasets. We show that Maximal Marginal Relevance (MMR), LexRank and Latent Semantic Analysis (LSA) all improve classification performance in both datasets used for testing.Comment: 12 pages, 1 table; Submitted to IEEE Signal Processing Letter

    Assessing User Expertise in Spoken Dialog System Interactions

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    Identifying the level of expertise of its users is important for a system since it can lead to a better interaction through adaptation techniques. Furthermore, this information can be used in offline processes of root cause analysis. However, not much effort has been put into automatically identifying the level of expertise of an user, especially in dialog-based interactions. In this paper we present an approach based on a specific set of task related features. Based on the distribution of the features among the two classes - Novice and Expert - we used Random Forests as a classification approach. Furthermore, we used a Support Vector Machine classifier, in order to perform a result comparison. By applying these approaches on data from a real system, Let's Go, we obtained preliminary results that we consider positive, given the difficulty of the task and the lack of competing approaches for comparison.Comment: 10 page
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