Un silicoflagelado en el Albiano medio de la Cuenca Austral, Argentina

Calcareous nannofossil biostratigraphic investigations of Cretaceous sediments within the southern high latitude Austral Basin, Argentina, have revealed the presence of a fragment of a silicoflagellate possibly belonging to the species Variramus aculeifera (Deflandre) McCartney, Wise, Harwood and Gersonde. Although only a fragment was identified in the samples examined from this locality, its presence is important, as it is only the second documented occurrence of an Early Cretaceous silicoflagellate. The specimen was found in a subsurface sample from the informal Palermo Aike Formation at Austral Basin. This stratigraphic level is interpreted as middle Albian on the basis of calcareous nannofossil biostratigraphy. The specimen consists of a fragmented strut with broken spines at the base, which allows the observation of the hollow skeletal structure. The strut is curved and slightly expanded at the widest part, which are characteristics diagnostic of the genus Variramus.Estudios sobre nanofósiles calcáreos en sedimentitas cretácicas de la Cuenca Austral, Argentina, revelaron la presencia de un fragmento de un silicoflagelado, posiblemente de Variramus aculeifera (Deflandre) McCartney, Wise, Harwood and Gersonde. A pesar de que se recuperó solo un fragmento, su presencia es importante porque representa el segundo registro de silicoflage-lados del Cretácico Temprano. El espécimen fue hallado en una muestra de subsuelo correspondiente a la Formación Palermo Aike, en la Cuenca Austral. Ese nivel estratigráfico fue interpretado como Albiano medio sobre la base de los nanofósiles calcáreos. El ejemplar corresponde al fragmento de una barra con espinas rotas en la base, que permitieron observar la naturaleza hueca del esqueleto silíceo. La barra es curva y presenta un leve ensanchamiento, dos características diagnósticas del género Variarmus.Fil: Perez Panera, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo; Argentin

Learning how to be robust: Deep polynomial regression

Polynomial regression is a recurrent problem with a large number of applications. In computer vision it often appears in motion analysis. Whatever the application, standard methods for regression of polynomial models tend to deliver biased results when the input data is heavily contaminated by outliers. Moreover, the problem is even harder when outliers have strong structure. Departing from problem-tailored heuristics for robust estimation of parametric models, we explore deep convolutional neural networks. Our work aims to find a generic approach for training deep regression models without the explicit need of supervised annotation. We bypass the need for a tailored loss function on the regression parameters by attaching to our model a differentiable hard-wired decoder corresponding to the polynomial operation at hand. We demonstrate the value of our findings by comparing with standard robust regression methods. Furthermore, we demonstrate how to use such models for a real computer vision problem, i.e., video stabilization. The qualitative and quantitative experiments show that neural networks are able to learn robustness for general polynomial regression, with results that well overpass scores of traditional robust estimation methods.Comment: 18 pages, conferenc

The Flow Fingerprinting Game

Linking two network flows that have the same source is essential in intrusion detection or in tracing anonymous connections. To improve the performance of this process, the flow can be modified (fingerprinted) to make it more distinguishable. However, an adversary located in the middle can modify the flow to impair the correlation by delaying the packets or introducing dummy traffic. We introduce a game-theoretic framework for this problem, that is used to derive the Nash Equilibrium. As obtaining the optimal adversary delays distribution is intractable, some approximations are done. We study the concrete example where these delays follow a truncated Gaussian distribution. We also compare the optimal strategies with other fingerprinting schemes. The results are useful for understanding the limits of flow correlation based on packet timings under an active attacker.Comment: Workshop on Information Forensics and Securit

Extending the scope of models for large-scale structure formation in the Universe

We propose a phenomenological generalization of the models of large-scale structure formation in the Universe by gravitational instability in two ways: we include pressure forces to model multi-streaming, and noise to model fluctuations due to neglected short-scale physical processes. We show that pressure gives rise to a viscous-like force of the same character as that one introduced in the ``adhesion model'', while noise leads to a roughening of the density field yielding a scaling behavior of its correlations.Comment: matches published version in A&A, incl. 3 figure