Video question answering supported by a multi-task learning objective

Video Question Answering (VideoQA) concerns the realization of models able to analyze a video, and produce a meaningful answer to visual content-related questions. To encode the given question, word embedding techniques are used to compute a representation of the tokens suitable for neural networks. Yet almost all the works in the literature use the same technique, although recent advancements in NLP brought better solutions. This lack of analysis is a major shortcoming. To address it, in this paper we present a twofold contribution about this inquiry and its relation with question encoding. First of all, we integrate four of the most popular word embedding techniques in three recent VideoQA architectures, and investigate how they influence the performance on two public datasets: EgoVQA and PororoQA. Thanks to the learning process, we show that embeddings carry question type-dependent characteristics. Secondly, to leverage this result, we propose a simple yet effective multi-task learning protocol which uses an auxiliary task defined on the question types. By using the proposed learning strategy, significant improvements are observed in most of the combinations of network architecture and embedding under analysis

Learning Video Retrieval Models with Relevance-Aware Online Mining

Due to the amount of videos and related captions uploaded every hour, deep learning-based solutions for cross-modal video retrieval are attracting more and more attention. A typical approach consists in learning a joint text-video embedding space, where the similarity of a video and its associated caption is maximized, whereas a lower similarity is enforced with all the other captions, called negatives. This approach assumes that only the video and caption pairs in the dataset are valid, but different captions - positives - may also describe its visual contents, hence some of them may be wrongly penalized. To address this shortcoming, we propose the Relevance-Aware Negatives and Positives mining (RANP) which, based on the semantics of the negatives, improves their selection while also increasing the similarity of other valid positives. We explore the influence of these techniques on two video-text datasets: EPIC-Kitchens-100 and MSR-VTT. By using the proposed techniques, we achieve considerable improvements in terms of nDCG and mAP, leading to state-of-the-art results, e.g. +5.3% nDCG and +3.0% mAP on EPIC-Kitchens-100. We share code and pretrained models at https://github.com/aranciokov/ranp

Observing the evaporation transition in vibro-fluidized granular matter

By shaking a sand box the grains on the top start to jump giving the picture of evaporating a sand bulk, and a gaseous transition starts at the surface granular matter (GM) bed. Moreover the mixture of the grains in the whole bed starts to move in a cooperative way which is far away from a Brownian description. In a previous work we have shown that the key element to describe the statistics of this behavior is the exclusion of volume principle, whereby the system obeys a Fermi configurational approach. Even though the experiment involves an archetypal non-equilibrium system, we succeeded in defining a global temperature, as the quantity associated to the Lagrange parameter in a maximum entropic statistical description. In fact in order to close our approach we had to generalize the equipartition theorem for dissipative systems. Therefore we postulated, found and measured a fundamental dissipative parameter, written in terms of pumping and gravitational energies, linking the configurational entropy to the collective response for the expansion of the centre of mass (c.m.) of the granular bed. Here we present a kinetic approach to describe the experimental velocity distribution function (VDF) of this non-Maxwellian gas of macroscopic Fermi-like particles (mFp). The evaporation transition occurs mainly by jumping balls governed by the excluded volume principle. Surprisingly in the whole range of low temperatures that we measured this description reveals a lattice-gas, leading to a packing factor, which is independent of the external parameters. In addition we measure the mean free path, as a function of the driving frequency, and corroborate our prediction from the present kinetic theory.Comment: 6 pages, 4 figures, submitted for publication September 1st, 200

Parasitism of Corn Earworm, Helicoverpa zea (Boddie) (Lepidoptera: Noctuidae), by Tachinid Flies in Cultivated Hemp

In a survey on hemp grown in western Kentucky we found an average of 27.8 CEW larvae per plant. We recorded 45% parasitism of CEW in these fields by two species of tachinid flies, Winthemia rufopicta and Lespesia aletiae. Most parasitized larvae were third to sixth instars at the time of collection. We found up to 22 tachinid eggs per host larva, 89% of which typically bore between 1 and 5 eggs on the thorax. 45.9% of CEW bearing eggs died. The number of tachinid eggs per host was unrelated to host body mass, but both the number of tachinid eggs and caterpillar body mass influenced CEW survival. Larger CEW often survived parasitism and the number of fly eggs was negatively related to survival rate. The emergence of adult flies was positively correlated with the number of eggs, but no influence of the host size was found. High mortality of CEW larvae and the parasitoids developing within them in this system suggests that secondary chemicals (or poor nutrition) of the hemp diet may be negatively affecting host and parasitoid development and influencing their interactions

Instability of the origami of a ferrofluid drop in a magnetic field

Capillary origami is the wrapping of an usual fluid drop by a planar elastic membrane due to the interplay between capillary and elastic forces. Here, we use a drop of magnetic fluid whose shape is known to strongly depend on an applied magnetic field. We study the quasi-static and dynamical behaviors of such a magnetic capillary origami. We report the observation of an overturning instability that the origami undergoes at a critical magnetic field. This instability is triggered by an interplay between magnetic and gravitational energies in agreement with the theory presented here. Additional effects of elasticity and capillarity on this instability are also discussed.Comment: in press in PRL (2011

Measuring The Evolutionary Rate Of Cooling Of ZZ Ceti

We have finally measured the evolutionary rate of cooling of the pulsating hydrogen atmosphere (DA) white dwarf ZZ Ceti (Ross 548), as reflected by the drift rate of the 213.13260694 s period. Using 41 yr of time-series photometry from 1970 November to 2012 January, we determine the rate of change of this period with time to be dP/dt = (5.2 +/- 1.4) x 10(-15) s s(-1) employing the O - C method and (5.45 +/- 0.79) x 10(-15) s s(-1) using a direct nonlinear least squares fit to the entire lightcurve. We adopt the dP/dt obtained from the nonlinear least squares program as our final determination, but augment the corresponding uncertainty to a more realistic value, ultimately arriving at the measurement of dP/dt = (5.5 +/- 1.0) x 10(-15) s s(-1). After correcting for proper motion, the evolutionary rate of cooling of ZZ Ceti is computed to be (3.3 +/- 1.1) x 10(-15) s s(-1). This value is consistent within uncertainties with the measurement of (4.19 +/- 0.73) x 10(-15) s s(-1) for another similar pulsating DA white dwarf, G 117-B15A. Measuring the cooling rate of ZZ Ceti helps us refine our stellar structure and evolutionary models, as cooling depends mainly on the core composition and stellar mass. Calibrating white dwarf cooling curves with this measurement will reduce the theoretical uncertainties involved in white dwarf cosmochronometry. Should the 213.13 s period be trapped in the hydrogen envelope, then our determination of its drift rate compared to the expected evolutionary rate suggests an additional source of stellar cooling. Attributing the excess cooling to the emission of axions imposes a constraint on the mass of the hypothetical axion particle.NSF AST-1008734, AST-0909107Norman Hackerman Advanced Research Program 003658-0252-2009Astronom