Optical Flow Requires Multiple Strategies (but only one network)

We show that the matching problem that underlies optical flow requires multiple strategies, depending on the amount of image motion and other factors. We then study the implications of this observation on training a deep neural network for representing image patches in the context of descriptor based optical flow. We propose a metric learning method, which selects suitable negative samples based on the nature of the true match. This type of training produces a network that displays multiple strategies depending on the input and leads to state of the art results on the KITTI 2012 and KITTI 2015 optical flow benchmarks

Low-Sensitivity Functions from Unambiguous Certificates

We provide new query complexity separations against sensitivity for total Boolean functions: a power $3$ separation between deterministic (and even randomized or quantum) query complexity and sensitivity, and a power $2.22$ separation between certificate complexity and sensitivity. We get these separations by using a new connection between sensitivity and a seemingly unrelated measure called one-sided unambiguous certificate complexity ($UC_{min}$). We also show that $UC_{min}$ is lower-bounded by fractional block sensitivity, which means we cannot use these techniques to get a super-quadratic separation between $bs(f)$ and $s(f)$. We also provide a quadratic separation between the tree-sensitivity and decision tree complexity of Boolean functions, disproving a conjecture of Gopalan, Servedio, Tal, and Wigderson (CCC 2016). Along the way, we give a power $1.22$ separation between certificate complexity and one-sided unambiguous certificate complexity, improving the power $1.128$ separation due to G\"o\"os (FOCS 2015). As a consequence, we obtain an improved $\Omega(\log^{1.22} n)$ lower-bound on the co-nondeterministic communication complexity of the Clique vs. Independent Set problem.Comment: 25 pages. This version expands the results and adds Pooya Hatami and Avishay Tal as author

Jacqueline Kahanoff and the demise of the Levantine

More than anything else, Jacqueline Kahanoff is associated with the term Levantinism and, more specifically, with turning the term, which for many years had a derogatory meaning, into a positive source of identity. However, this reading of Kahanoff – namely, a carrier of the message of Levantinism as a bridge between Orient and Occident – seems to tell us more about Kahanoff’s readers than about Kahanoff herself. A careful reading of her writings reveals a different Kahanoff, a person who, more than being the originator and proponent of a new kind of identity, while moving swiftly across cultures and feeling at home nowhere because her home was everywhere, was actually well entrenched in the west, in Zionism and in Israel

Phonological (un)certainty weights lexical activation

Spoken word recognition involves at least two basic computations. First is matching acoustic input to phonological categories (e.g. /b/, /p/, /d/). Second is activating words consistent with those phonological categories. Here we test the hypothesis that the listener's probability distribution over lexical items is weighted by the outcome of both computations: uncertainty about phonological discretisation and the frequency of the selected word(s). To test this, we record neural responses in auditory cortex using magnetoencephalography, and model this activity as a function of the size and relative activation of lexical candidates. Our findings indicate that towards the beginning of a word, the processing system indeed weights lexical candidates by both phonological certainty and lexical frequency; however, later into the word, activation is weighted by frequency alone.Comment: 6 pages, 4 figures, accepted at: Cognitive Modeling and Computational Linguistics (CMCL) 201

Uncertainty Quantification in Composite Materials

The random nature of the micro-structural attributes in materials in general and composite material systems in particular requires expansion of material modeling in a way that will incorporate their inherent uncertainty and predict its impact on material properties and mechanical response in multiple scales. Despite the importance of capturing and modeling material randomness, there are numerous challenges in structural characterization that are yet to be addressed. The work presented in this essay takes a few steps towards an improved material modeling approach which encompasses structural randomness in order to produce a more realistic representation of material systems. For this end a computational framework was developed to generate a realistic representative volume element which reflects the inherent structural randomness. First stochastic structural elements were identified and registered from imaging data and parameters were assigned to represent those elements. Statistical characterization of the random attributes was followed by the construction of a representative volume element which shared the same structural statistical characteristics with the original material system. The resultant statistical equivalent representative volume element (SERVE) was then used in finite element simulations which provided homogenized properties and mechanical response predictions. The suggested framework was developed and then implemented on 3 different material systems. Image processing and analysis in one of the material systems extended the original scope of this work to solving a machine vision and learning problem. Object segmentation for the purpose object and pattern recognition has been a long standing subject of interest in the field of machine vision. Despite the significant attention given to the development of segmentation and recognition methods, the critical challenge of separating merged objects did not share the spotlight. A simple yet original approach to overcome this hurdle was developed using unsupervised classification and separation of objects in 3D. Lower dimensionality classifiers were joined to provide a powerful higher dimensionality classification tool. The robustness of this approach is illustrated through its implementation on two case studies of merged objects. Applications of this methodology can further extend from structural classification to general problems of clustering and classification in various fields

Dynamical evolution of the young stars in the Galactic center

Recent observations of the Galactic center revealed a nuclear disk of young OB stars near the massive black hole (MBH), in addition to many similar outlying stars with higher eccentricities and/or high inclinations relative to the disk (some of them possibly belonging to a second disk). In addition, observations show the existence of young B stars (the 'S-cluster') in an isotropic distribution in the close vicinity of the MBH ($<0.04$ pc). We use extended N-body simulations to probe the dynamical evolution of these two populations. We show that the stellar disk could have evolved to its currently observed state from a thin disk of stars formed in a gaseous disk, and that the dominant component in its evolution is the interaction with stars in the cusp around the MBH. We also show that the currently observed distribution of the S-stars could be consistent with a capture origin through 3-body binary-MBH interactions. In this scenario the stars are captured at highly eccentric orbits, but scattering by stellar black holes could change their eccentricity distribution to be consistent with current observations.Comment: 5 pages, 2 figures. To appear in the proceedings of the Central Kiloparsec conference, 2008, Cret

The effectiveness of hedging foreign exchange rate risk: an emerging market perspective

Thesis (M.M. (Finance & Investment))--University of the Witwatersrand, Faculty of Commerce, Law and Management, Graduate School of Business Administration, 2013.This study provides an analysis of the effectiveness of the foreign currency hedging abilities afforded by the futures market. The focus is on the currencies of six emerging markets, namely; Brazil, India, Mexico, Russia, South Africa and Turkey. By examining emerging market currencies we can examine the effect that possible mispricing and lack of liquidity can have on hedging effectiveness. To this effect, this article uses the regression method, as allowed by the accounting standard FAS 133, to assess the effectiveness of futures contracts as a hedging mechanism for emerging market currencies. The methods follow previous studies such as Hill and Schneeweis (1982) which consider the length of the hedging horizon and time to expiration due to their effect on hedge effectiveness. Results indicate consistent hedge effectiveness in only South Africa and Turkey, with reasonable hedge effectiveness exhibited by Mexico and Russia. Sensible explanations are given for the extreme hedge ineffectiveness that can be seen in the Brazilian and Indian tests