MIAMI – People as old as 79 may still generate new brain cells, U.S. researchers said Thursday, stoking fresh debate among scientists over whether or when our mental capacity ever stops growing. The report by scientists at Columbia University in New York, published in the journal Cell Stem Cell, runs directly counter to a different study published in Nature last month which found no evidence of new neurons are being created past the age of 13. While neither study is seen as providing the definitive last word, the research is being closely watched as the world's population ages and scientists seek to better understand how the brain ages for clues to ward off dementia. The focal point of the research is the hippocampus, the brain's center for learning and memory. Specifically, researchers are looking for the foundations of new brain cells, including progenitor cells, or stem cells that would eventually become neurons.
Azaria, Amos (Bar Ilan University) | Hassidim, Avinatan (Bar Ilan University) | Kraus, Sarit (Bar Ilan University) | Eshkol, Adi (Viaccess-Orca) | Weintraub, Ofer (Viaccess-Orca) | Netanely, Irit (Viaccess-Orca)
Traditional recommender systems try to provide users with recommendations which maximize the probability that the user will accept them. Recent studies have shown that recommender systems have a positive effect on the provider’s revenue. In this paper we show that by giving a different set of recommendations, the recommendation system can further increase the business’ utility (e.g. revenue), without any significant drop in user satisfaction. Indeed, the recommendation system designer should have in mind both the user, whose taste we need to reveal, and the business, which wants to promote specific content. In order to study these questions, we performed a large body of experiments on Amazon Mechanical Turk. In each of the experiments, we compare a commercial state-of-the-art recommendation engine with a modified recommendation list, which takes into account the utility (or revenue) which the business obtains from each suggestion that is accepted by the user. We show that the modified recommendation list is more desirable for the business, as the end result gives the business a higher utility (or revenue). To study possible longterm effects of giving the user worse suggestions, we asked the users how they perceive the list of recommendation that they received. Our findings are that any difference in user satisfaction between the list is negligible, and not statistically significant. We also uncover a phenomenon where movie consumers prefer watching and even paying for movies that they have already seen in the past than movies that are new to them.
We conducted two studies to examine gender differences in in response to Facebook status updates. The first study surveyed 600 undergraduate students (388 females and 207 males), and analysed males’ and females’ responses to Facebook status updates. Females were significantly more likely to post a public reply than males, and female public replies also contained higher levels of emotional support. There were no significant gender differences in private replies to Facebook status updates. Males showed significantly higher levels of emotional support in private messages than in public replies. There was no significant difference in terms of level of emotional support between females’ public replies and private messages. The second study investigated gender differences in response to Facebook status updates from same gender friends compared to opposite gender friends. We surveyed 522 undergraduate students (216 females and 306 males), and analysed males’ and females’ responses to two Facebook status updates: one from a same gender close friend and one from an opposite gender close friend. Females showed higher levels of emotional support than males to a Facebook status update from a same gender friend. In contrast, there were no significant gender differences in response to an opposite gender friend. Males showed higher levels of emotional support in private replies than public replies to same gender friends. There was no difference in level of emotional support between females’ public replies and private messages. The implications of these findings for explanations of gender differences in language use are discussed
We evaluate the impact of tutor voice quality in the context of our intelligent tutoring spoken dialogue system. We first describe two versions of our system which yielded two corpora of human-computer tutoring dialogues: one using a tutor voice prerecorded by a human, and the other using a lowcost text-to-speech tutor voice. We then discuss the results of two-tailed t-tests comparing student learning gains, system usability, and dialogue efficiency across the two corpora and across corpora subsets. Overall, our results suggest that tutor voice quality may have only a minor impact on these metrics in the context of our tutoring system. We find that tutor voice quality does not impact learning gains, but it may impact usability and efficiency for some corpora subsets.
Traditional techniques for monitoring wildlife populations are temporally and spatially limited. Alternatively, in order to quickly and accurately extract information about the current state of the environment, tools for processing and recognition of acoustic signals can be used. In the past, a number of research studies on automatic classification of species through their vocalizations have been undertaken. In many of them, however, the segmentation applied in the preprocessing stage either implies human effort or is insufficiently described to be reproduced. Therefore, it might be unfeasible in real conditions. Particularly, this paper is focused on the extraction of local information as units --called instances-- from audio recordings. The methodology for instance extraction consists in the segmentation carried out using image processing techniques on spectrograms and the estimation of a needed threshold by the Otsu's method. The multiple instance classification (MIC) approach is used for the recognition of the sound units. A public data set was used for the experiments. The proposed unsupervised segmentation method has a practical advantage over the compared supervised method, which requires the training from manually segmented spectrograms. Results show that there is no significant difference between the proposed method and its baseline. Therefore, it is shown that the proposed approach is feasible to design an automatic recognition system of recordings which only requires, as training information, labeled examples of audio recordings.