Is Hanukkah responsive to Christmas?

We study the extent to which religious activity responds to the presence and activity of other religions. Specifcally, we employ individual-level survey data and county-level expenditure data to examine the extent to which Hanukkah celebration among U.S. Jews is driven by the presence of Christmas. We find that: (1) Jews with children at home are more likely to celebrate Hanukkah than Jews without children. (2) The effect of having children on Hanukkah celebrations is higher for reform Jews than for orthodox Jews; and, it is higher for Jews who feel a stronger sense of belonging to Judaism. (3) Jewish-related expenditures in Hanukkah are higher in counties with lower share of Jews. These findings are consistent with the hypothesis that Jews increase religious activity during Hanukkah because of the presence of Christmas, and this response is primarily driven by the presence of children at home. One underlying motivator might be that Jewish parents in the U.S. celebrate Hanukkah more intensively so heir children do not feel left out, and/or because they are concerned that their children will convert or intermarry.Religions, Hanukkah, Identity

A Faster FPTAS for #Knapsack

Given a set W = {w_1,..., w_n} of non-negative integer weights and an integer C, the #Knapsack problem asks to count the number of distinct subsets of W whose total weight is at most C. In the more general integer version of the problem, the subsets are multisets. That is, we are also given a set {u_1,..., u_n} and we are allowed to take up to u_i items of weight w_i. We present a deterministic FPTAS for #Knapsack running in O(n^{2.5}epsilon^{-1.5}log(n epsilon^{-1})log (n epsilon)) time. The previous best deterministic algorithm [FOCS 2011] runs in O(n^3 epsilon^{-1} log(n epsilon^{-1})) time (see also [ESA 2014] for a logarithmic factor improvement). The previous best randomized algorithm [STOC 2003] runs in O(n^{2.5} sqrt{log (n epsilon^{-1})} + epsilon^{-2} n^2) time. Therefore, for the case of constant epsilon, we close the gap between the O~(n^{2.5}) randomized algorithm and the O~(n^3) deterministic algorithm. For the integer version with U = max_i {u_i}, we present a deterministic FPTAS running in O(n^{2.5}epsilon^{-1.5}log(n epsilon^{-1} log U)log (n epsilon) log^2 U) time. The previous best deterministic algorithm [TCS 2016] runs in O(n^3 epsilon^{-1}log(n epsilon^{-1} log U) log^2 U) time

Speaker-independent Speech Inversion for Estimation of Nasalance

The velopharyngeal (VP) valve regulates the opening between the nasal and oral cavities. This valve opens and closes through a coordinated motion of the velum and pharyngeal walls. Nasalance is an objective measure derived from the oral and nasal acoustic signals that correlate with nasality. In this work, we evaluate the degree to which the nasalance measure reflects fine-grained patterns of VP movement by comparison with simultaneously collected direct measures of VP opening using high-speed nasopharyngoscopy (HSN). We show that nasalance is significantly correlated with the HSN signal, and that both match expected patterns of nasality. We then train a temporal convolution-based speech inversion system in a speaker-independent fashion to estimate VP movement for nasality, using nasalance as the ground truth. In further experiments, we also show the importance of incorporating source features (from glottal activity) to improve nasality prediction.Comment: Interspeech 202

Chameleo : walk like a chameleon detection with AI

Extensive research over the last few years has revealed the potential of computational based models to understand animal behavior. However, computational ethology using reptiles as models is still under investigation. Chameleons are known to have slow arboreal locomotion and present a distinct movement of rocking back-and-forth in between periods of the traditional quadrupedal walk. This curious, and yet, under-investigated behavior known as “leaf movement”, has been observed in different species of the genus Chamaeleo. Here we present our work-in-progress and propose the means to quantitatively examine plausible gaits of chameleons using an Artificial Neural Network system named Chameleo. We recorded and labeled around 8 hours of chameleons moving horizontally on a rope in an experimental setup and aim to use this data for training and further testing of the Neural Network. We expect that Chameleo will be an accurate and reliable model for the identification and classification of chameleon locomotion. Furthermore, our long-term goals are to 1) adapt Chameleo to a wider range of lizard behaviors, 2) make the model available for the scientific community through a website where researchers will be able to add additional models and datasets to further explore reptile behavior, 3) contribute to the welfare of pet chameleons, and finally 4) encourage citizen science and thus conservation and environmental protection of the species

Estimating Sizes of Social Networks via Biased Sampling

Online social networks have become very popular in recent years and their number of users is already measured in many hundreds of millions. For various commercial and sociological purposes, an independent estimate of their sizes is important. In this work, algorithms for estimating the number of users in such networks are considered. The proposed schemes are also applicable for estimating the sizes of networks’ sub-populations. The suggested algorithms interact with the social networks via their public APIs only, and rely on no other external information. Due to obvious traffic and privacy concerns, the number of such interactions is severely limited. We therefore focus on minimizing the number of API interactions needed for producing good size estimates. We adopt the abstraction of social networks as undirected graphs and use random node sampling. By counting the number of collisions or non-unique nodes in the sample, we produce a size estimate. Then, we show analytically that the estimate error vanishes with high probability for smaller number of samples than those required by prior-art algorithms. Moreover, although our algorithms are provably correct for any graph, theyexcelwhenappliedtosocial network-likegraphs. The proposed algorithms were evaluated on syntheticas well real social networks such as Facebook, IMDB, and DBLP. Our experiments corroborated the theoretical results, and demonstrated the effectiveness of the algorithms

Estimating Sizes of Social Networks via Biased Sampling

Online social networks have become very popular in recent years and their number of users is already measured in many hundreds of millions. For various commercial and sociological purposes, an independent estimate of their sizes is important. In this work, algorithms for estimating the number of users in such networks are considered. The proposed schemes are also applicable for estimating the sizes of networks’ sub-populations. The suggested algorithms interact with the social networks via their public APIs only, and rely on no other external information. Due to obvious traffic and privacy concerns, the number of such interactions is severely limited. We therefore focus on minimizing the number of API interactions needed for producing good size estimates. We adopt the abstraction of social networks as undirected graphs and use random node sampling. By counting the number of collisions or non-unique nodes in the sample, we produce a size estimate. Then, we show analytically that the estimate error vanishes with high probability for smaller number of samples than those required by prior-art algorithms. Moreover, although our algorithms are provably correct for any graph, they excel when applied to social network-like graphs. The proposed algorithms were evaluated on synthetic as well real social networks such as Facebook, IMDB, and DBLP. Our experiments corroborated the theoretical results, and demonstrated the effectiveness of the algorithms