Evidence of two effects in the size segregation process in a dry granular media

submitted to Physical Review E (february 2004)In a half-filled rotating drum, the size segregation of particles of equal density builds a ring pattern of the large particles, whose location continuously varies from the periphery to the center depending on the size ratio between particles [Thomas, Phys. Rev. E 62, 1 (2000) 961-974]. For small size ratios (typically15): large beads are close to the bottom in a reversing position. The existence of circles with an intermediate radius shows that the segregation at an intermediate level within a flow is possible. In this paper, we experimentally study the segregation of particles of different densities and sizes in a half-filled rotating drum and other devices (chute flow, pile). In the drum, the location of the segregated ring continuously varies from the periphery to the center and is very sensitive to both the size (from 1 to 33) and density (from 0.36 to 4.8) ratios. The densest large beads segregate on a circle close to the center, the lightest large beads on a circle close to the periphery. Consequently, we found that for any tracer, its excess of mass, due to only a size excess, a density excess, or both, leads to a deep inside segregation of the tracer. There is a push-away process that makes heavy beads of any type go downwards, while the excess of size is already known to push large beads towards the surface, by a dynamical sieving process. Each segregation at an intermediate ring corresponds to a balance between these mass and geometrical effects. The segregation level in the flow is determined by the ratio of the intensities of both effects

Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented Approach

Goals are first-class entities in a self-adaptive system (SAS) as they guide the self-adaptation. A SAS often operates in dynamic and partially unknown environments, which cause uncertainty that the SAS has to address to achieve its goals. Moreover, besides the environment, other classes of uncertainty have been identified. However, these various classes and their sources are not systematically addressed by current approaches throughout the life cycle of the SAS. In general, uncertainty typically makes the assurance provision of SAS goals exclusively at design time not viable. This calls for an assurance process that spans the whole life cycle of the SAS. In this work, we propose a goal-oriented assurance process that supports taming different sources (within different classes) of uncertainty from defining the goals at design time to performing self-adaptation at runtime. Based on a goal model augmented with uncertainty annotations, we automatically generate parametric symbolic formulae with parameterized uncertainties at design time using symbolic model checking. These formulae and the goal model guide the synthesis of adaptation policies by engineers. At runtime, the generated formulae are evaluated to resolve the uncertainty and to steer the self-adaptation using the policies. In this paper, we focus on reliability and cost properties, for which we evaluate our approach on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the validation are promising and show that our approach is able to systematically tame multiple classes of uncertainty, and that it is effective and efficient in providing assurances for the goals of self-adaptive systems

Nilpotent subgroups of the group of fibre homotopy equivalences

Let $\xi=(E,p,B,F)$ be a Hurewicz fibration. In this paper we study the space \Cal L_G(\xi) consisting of fibre homotopy self equivalences of $\xi$ inducing by restriction to the fibre a self homotopy equivalence of $F$ belonging to the group $G$. We give in particular conditions implying that \pi_1(\Cal L_G(\xi)) is finitely generated or that \Cal L_1(\xi) has the same rational homotopy type as $\operatorname{aut}_1(F)$

On the algebraic approximation of Lusternik-Schnirelmann category

Algebraic approximations have proved to be very useful in the investigation of Lusternik-Schnirelmann category. In this paper the L.-S. category and its approximations are studied from the point of view of abstract homotopy theory. We introduce three notions of L.-S. category for monoidal cofibration categories, i.e., cofibration categories with a suitably incorporated tensor product. We study the fundamental properties of the abstract invariants and discuss, in particular, their behaviour with respect to cone attachments and products. Besides the topological L.-S. category the abstract concepts cover classical algebraic approximations of the L.-S. category such as the Toomer invariant, rational category, and the A- and M-categories of Halperin and Lemaire. We also use the abstract theory to introduce a new algebraic approximation of L.-S. category. This invariant which we denote by $\ell$ is the first algebraic approximation of the L.-S. category which is not necessarily $\leq 1$ for spaces having the same Adams-Hilton model as a wedge of spheres. For a space $X$ the number $\ell (X)$ can be determined from an Anick model of $X$. Thanks to the general theory one knows \textit{a priori} that $\ell$ is a lower bound of the L.-S. category which satisfies the usual product inequality and increases by at most 1 when a cone is attached to a space

Low-complexity Overfitted Neural Image Codec

We propose a neural image codec at reduced complexity which overfits the decoder parameters to each input image. While autoencoders perform up to a million multiplications per decoded pixel, the proposed approach only requires 2300 multiplications per pixel. Albeit low-complexity, the method rivals autoencoder performance and surpasses HEVC performance under various coding conditions. Additional lightweight modules and an improved training process provide a 14% rate reduction with respect to previous overfitted codecs, while offering a similar complexity. This work is made open-source at https://orange-opensource.github.io/Cool-Chic/Comment: Accepted at IEEE MMSP 202