Modeling the input history of programs for improved instruction-memory performance

When a program is loaded into memory for execution, the relative position of its basic blocks is crucial, since loading basic blocks that are unlikely to be executed first places them high in the instruction-memory hierarchy only to be dislodged as the execution goes on. In this paper we study the use of Bayesian networks as models of the input history of a program. The main point is the creation of a probabilistic model that persists as the program is run on different inputs and at each new input refines its own parameters in order to reflect the program's input history more accurately. As the model is thus tuned, it causes basic blocks to be reordered so that, upon arrival of the next input for execution, loading the basic blocks into memory automatically takes into account the input history of the program. We report on extensive experiments, whose results demonstrate the efficacy of the overall approach in progressively lowering the execution times of a program on identical inputs placed randomly in a sequence of varied inputs. We provide results on selected SPEC CINT2000 programs and also evaluate our approach as compared to the gcc level-3 optimization and to Pettis-Hansen reordering

Characterizing neuromorphologic alterations with additive shape functionals

The complexity of a neuronal cell shape is known to be related to its function. Specifically, among other indicators, a decreased complexity in the dendritic trees of cortical pyramidal neurons has been associated with mental retardation. In this paper we develop a procedure to address the characterization of morphological changes induced in cultured neurons by over-expressing a gene involved in mental retardation. Measures associated with the multiscale connectivity, an additive image functional, are found to give a reasonable separation criterion between two categories of cells. One category consists of a control group and two transfected groups of neurons, and the other, a class of cat ganglionary cells. The reported framework also identified a trend towards lower complexity in one of the transfected groups. Such results establish the suggested measures as an effective descriptors of cell shape

The evolution of the Sun's birth cluster and the search for the solar siblings with Gaia

We use self-consistent numerical simulations of the evolution and disruption of the Sun's birth cluster in the Milky Way potential to investigate the present-day phase space distribution of the Sun's siblings. The simulations include the gravitational N-body forces within the cluster and the effects of stellar evolution on the cluster population. In addition the gravitational forces due to the Milky Way potential are accounted for in a self-consistent manner. Our aim is to understand how the astrometric and radial velocity data from the Gaia mission can be used to pre-select solar sibling candidates. We vary the initial conditions of the Sun's birth cluster, as well as the parameters of the Galactic potential. We show that the disruption time-scales of the cluster are insensitive to the details of the non-axisymmetric components of the Milky Way model and we make predictions, averaged over the different simulated possibilities, about the number of solar siblings that should appear in surveys such as Gaia or GALAH. We find a large variety of present-day phase space distributions of solar siblings, which depend on the cluster initial conditions and the Milky Way model parameters. We show that nevertheless robust predictions can be made about the location of the solar siblings in the space of parallaxes ($\varpi$), proper motions ($\mu$) and radial velocities ($V_\mathrm{r}$). By calculating the ratio of the number of simulated solar siblings to that of the number of stars in a model Galactic disk, we find that this ratio is above 0.5 in the region given by: $\varpi \geq 5$mas, $4 \leq \mu \leq 6$masyr$^{-1}$, and $-2\leq V_\mathrm{r} \leq 0$kms$^{-1}$. Selecting stars from this region should increase the probability of success in identifying solar siblings through follow up observations [Abridged].Comment: 13 pages, 7 figures. Accepted for publication in MNRA

Diffusion Enhancement in Core-softened fluid confined in nanotubes

We study the effect of confinement in the dynamical behavior of a core-softened fluid. The fluid is modeled as a two length scales potential. This potential in the bulk reproduces the anomalous behavior observed in the density and in the diffusion of liquid water. A series of $NpT$ Molecular Dynamics simulations for this two length scales fluid confined in a nanotube were performed. We obtain that the diffusion coefficient increases with the increase of the nanotube radius for wide channels as expected for normal fluids. However, for narrow channels, the confinement shows an enhancement in the diffusion coefficient when the nanotube radius decreases. This behavior, observed for water, is explained in the framework of the two length scales potential.Comment: 17 pages, 8 figures, accept for publication at J. Chem. Phy

Heterosis in maize single crosses derived from a yellow Tuxpeño variety in Brazil.

Most maize (Zea mays L.) crosses in Tropical regions use the heterotic pattern of Tupeno dent and Caribbean flint. Corsses between related lines are not used for commercial production. Related inbred lines are used in either double or three-way hybrids with other unrelated lines to develop superior hybrids. This study was conducted to determine the combining ability among 11 related inbred lines from a Tuxpeno population. The 11 inbred lines were crossed in a diallel series and were evaluated at six locations. A combinig ability analysis was made for grain yeild. The average yield across environments for the 55 single (44.8 q/ha) was not comparable to that of the single-cross hybrid (56.5 q/ha), induced as check. General combining ability (GCA) effects and specific combining ability (SCA) effects were highly significant (P < 0.01). Variation due to GCA, however, accounted for 68% of the variation among crosses. Indicating that additive genetic effects were more important than nonadditive effects. Highly significantly positive GCA effects were observed for lines 6 (2.44 q/ha) and 7 (6.40 q/ha) and highly significantly negative GCA effects for lines 5 (1.63 q/ha), 10 (2.64 q/ha), and 11 (4,01 q/ha). Significantly positive SCA effects were observed with line 4 x line 11, line 5 x line 9, and line 5 x 11 crosses. Lines 6 and 7 may have potential use as parents for three-way or double-cross hybrids

Optimization of supply diversity for the self-assembly of simple objects in two and three dimensions

The field of algorithmic self-assembly is concerned with the design and analysis of self-assembly systems from a computational perspective, that is, from the perspective of mathematical problems whose study may give insight into the natural processes through which elementary objects self-assemble into more complex ones. One of the main problems of algorithmic self-assembly is the minimum tile set problem (MTSP), which asks for a collection of types of elementary objects (called tiles) to be found for the self-assembly of an object having a pre-established shape. Such a collection is to be as concise as possible, thus minimizing supply diversity, while satisfying a set of stringent constraints having to do with the termination and other properties of the self-assembly process from its tile types. We present a study of what we think is the first practical approach to MTSP. Our study starts with the introduction of an evolutionary heuristic to tackle MTSP and includes results from extensive experimentation with the heuristic on the self-assembly of simple objects in two and three dimensions. The heuristic we introduce combines classic elements from the field of evolutionary computation with a problem-specific variant of Pareto dominance into a multi-objective approach to MTSP.Comment: Minor typos correcte