Bounds on the dragging rate and on the rotational mass-energy in slowly and differentially rotating relativistic stars

For relativistic stars rotating slowly and differentially with a positive angular velocity, some properties in relation to the positiveness of the rate of rotational dragging and of the angular momentum density are derived. Also, a new proof for the bounds on the rotational mass-energy is given.Comment: 23 pages, latex. Submitted to J. Math. Phy

Relativistic stars in differential rotation: bounds on the dragging rate and on the rotational energy

For general relativistic equilibrium stellar models (stationary axisymmetric asymptotically flat and convection-free) with differential rotation, it is shown that for a wide class of rotation laws the distribution of angular velocity of the fluid has a sign, say "positive", and then both the dragging rate and the angular momentum density are positive. In addition, the "mean value" (with respect to an intrinsic density) of the dragging rate is shown to be less than the mean value of the fluid angular velocity (in full general, without having to restrict the rotation law, nor the uniformity in sign of the fluid angular velocity); this inequality yields the positivity and an upper bound of the total rotational energy.Comment: 23 pages, no figures, LaTeX. Submitted to J. Math. Phy

Algorithmic Debugging of Real-World Haskell Programs: Deriving Dependencies from the Cost Centre Stack

Existing algorithmic debuggers for Haskell require a transformation of all modules in a program, even libraries that the user does not want to debug and which may use language features not supported by the debugger. This is a pity, because a promising ap- proach to debugging is therefore not applicable to many real-world programs. We use the cost centre stack from the Glasgow Haskell Compiler profiling environment together with runtime value observations as provided by the Haskell Object Observation Debugger (HOOD) to collect enough information for algorithmic debugging. Program annotations are in suspected modules only. With this technique algorithmic debugging is applicable to a much larger set of Haskell programs. This demonstrates that for functional languages in general a simple stack trace extension is useful to support tasks such as profiling and debugging

Response of the parasitoid Telenomus podisi to induced volatiles from soybean damaged by stink bug herbivory and oviposition.

Egg parasitoids have a short time frame in which their host eggs are suitable for parasitism, and in several systems these parasitoids respond to plant volatiles induced by oviposition on the plant (either in isolation or in combination with feeding damage) as a means of finding suitable hosts. It is known that the parasitoid of pentatomid eggs Telenomus podisi responds to damage done to soybeans by female Euschistus heros, its preferred host. ln this study our aim was to determine the type of E. heros damage to soybean (herbivory, oviposition or a combination of both) necessary for attraction of T. podisi. ln a Y-tube olfactometer the parasitoid has shown to respond to the undamaged plant over clcan air and herbivory-damaged plants over undamaged plants. However, the parasitoids did not respond to the treatments wherc oviposition occurred, either in isolation or in combination with herbivory. Analysis of volatile blends revealed that herbivory plus oviposition damage to soybean induced a volatile blend different to those induced when herbivory or oviposition occurred separately. These results, along with other results from this system, suggest that T. podisi uses plant volatile cues associated with female E. heros damage in order to be present when E. heros lays its eggs, and thus ensure its resource is optimal for parasitism

Response of the parasitoid Telenomus podisi to induced volatiles from soybean damaged by stink bug herbivory and oviposition.

Egg parasitoids have a short time frame in which their host eggs are suitable for parasitism, and in several systems these parasitoids respond to plant volatiles induced by oviposition on the plant (either in isolation or in combination with feeding damage) as a means of finding suitable hosts. It is known that the parasitoid of pentatomid eggs Telenomus podisi responds to damage done to soybeans by female Euschistus heros, its preferred host. ln this study our aim was to determine the type of E. heros damage to soybean (herbivory, oviposition or a combination of both) necessary for attraction of T. podisi. ln a Y-tube olfactometer the parasitoid has shown to respond to the undamaged plant over clcan air and herbivory-damaged plants over undamaged plants. However, the parasitoids did not respond to the treatments wherc oviposition occurred, either in isolation or in combination with herbivory. Analysis of volatile blends revealed that herbivory plus oviposition damage to soybean induced a volatile blend different to those induced when herbivory or oviposition occurred separately. These results, along with other results from this system, suggest that T. podisi uses plant volatile cues associated with female E. heros damage in order to be present when E. heros lays its eggs, and thus ensure its resource is optimal for parasitism

Effect of biliopancreatic diversion on sleep quality and daytime sleepiness in patients with obesity and type 2 diabetes

sem informaçãoThe poor quality of sleep and the deprivation thereof have been associated with disruption of metabolic homeostasis, favoring the development of obesity and type 2 diabetes (T2DM). We aimed to evaluate the influence of biliopancreatic diversion (BPD) surg616623627sem informaçãosem informaçãosem informaçã

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

Graph representation learning resurges as a trending research subject owing to the widespread use of deep learning for Euclidean data, which inspire various creative designs of neural networks in the non-Euclidean domain, particularly graphs. With the success of these graph neural networks (GNN) in the static setting, we approach further practical scenarios where the graph dynamically evolves. Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics. These methods require the knowledge of a node in the full time span (including both training and testing) and are less applicable to the frequent change of the node set. In some extreme scenarios, the node sets at different time steps may completely differ. To resolve this challenge, we propose EvolveGCN, which adapts the graph convolutional network (GCN) model along the temporal dimension without resorting to node embeddings. The proposed approach captures the dynamism of the graph sequence through using an RNN to evolve the GCN parameters. Two architectures are considered for the parameter evolution. We evaluate the proposed approach on tasks including link prediction, edge classification, and node classification. The experimental results indicate a generally higher performance of EvolveGCN compared with related approaches. The code is available at \url{https://github.com/IBM/EvolveGCN}.Comment: AAAI 2020. The code is available at https://github.com/IBM/EvolveGC