Tropidosteptes forestierae (Hemiptera: Heteroptera: Miridae: Mirinae): a new species of Plant Bug injuring ornamental Florida Swampprivet, Forestiera segregata (Oleaceae), in South Florida

The mirine plant bug Tropidosteptes forestierae, new species (Hemiptera: Miridae) is described from Collier County, Florida, where it was found causing serious injury to an extensive ornamental hedge of Florida swampprivet, Forestiera segregata (Jacq.) Krug and Urb. (Oleaceae). Adult male and female, fifth instar, and egg are described. Color images of the adults, nymph, egg, and injury; scanning photomicrographs of selected adult structures; and illustrations of male genitalia are provided. A key to help distinguish the 16 species of Tropidosteptes known to occur in the southeastern United States is given

Experiment to evaluate feasibility of utilizing Skylab-EREP remote sensing data for tectonic analysis of the Bighorn Mountains region, Wyoming-Montana

There are no author-identified significant results in this report

Learning in stomatopod crustaceans

The stomatopod crustaceans, or mantis shrimps, are marine predators that stalk or ambush prey and that have complex intraspecific communication behavior. Their active lifestyles, means of predation, and intricate displays all require unusual flexibility in interacting with the world around them, implying a well-developed ability to learn. Stomatopods have highly evolved sensory systems, including some of the most specialized visual systems known for any animal group. Some species have been demonstrated to learn how to recognize and use novel, artificial burrows, while others are known to learn how to identify novel prey species and handle them for effective predation. Stomatopods learn the identities of individual competitors and mates, using both chemical and visual cues. Furthermore, stomatopods can be trained for psychophysical examination of their sensory abilities, including demonstration of color and polarization vision. These flexible and intelligent invertebrates continue to be attractive subjects for basic research on learning in animals with relatively simple nervous systems

High resolution, low temperature photoabsorption cross-section of C2H2 with application to Saturn's atmosphere

New laboratory observations of the VUV absorption cross-section of C2H2, obtained under physical conditions approximating stratospheres of the giant planets, were combined with IUE observations of the albedo of Saturn, for which improved data reduction techniques have been used, to produce new models for that atmosphere. When the effects of C2H2 absorption are accounted for, additional absorption by other molecules is required. The best-fitting model also includes absorption by PH3, H2O, C2H6 and CH4. A small residual disagreement near 1600 A suggests that an additional trace species may be required to complete the model

Fluvio-deltaic avulsions during relative sea-level fall.

Understanding river response to changes in relative sea level (RSL) is essential for predicting fluvial stratigraphy and source-to-sink dynamics. Recent theoretical work has suggested that rivers can remain aggradational during RSL fall, but field data are needed to verify this response and investigate sediment deposition processes. We show with field work and modeling that fluvio-deltaic systems can remain aggradational or at grade during RSL fall, leading to superelevation and continuation of delta lobe avulsions. The field site is the Goose River, Newfoundland-Labrador, Canada, which has experienced steady RSL fall of around 3–4 mm yr⁻¹ in the past 5 k.y. from post-glacial isostatic rebound. Elevation analysis and optically stimulated luminescence dating suggest that the Goose River avulsed and deposited three delta lobes during RSL fall. Simulation results from Delft3D software show that if the characteristic fluvial response time is longer than the duration of RSL fall, then fluvial systems remain aggradational or at grade, and continue to avulse during RSL fall due to superelevation. Intriguingly, we find that avulsions become more frequent at faster rates of RSL fall, provided the system response time remains longer than the duration of RSL fall. This work suggests that RSL fall rate may influence the architecture of falling-stage or forced regression deposits by controlling the number of deposited delta lobes

The effects of Chern-Simons gravity on bodies orbiting the Earth

One of the possible low-energy consequences of string theory is the addition of a Chern-Simons term to the standard Einstein-Hilbert action of general relativity. It can be argued that the quintessence field should couple to this Chern-Simons term, and if so, it drives in the linearized theory a parity-violating interaction between the gravito-electric and gravitomagnetic fields. In this paper, the linearized spacetime for Chern-Simons gravity around a massive spinning body is found to include new modifications to the gravitomagnetic field that have not appeared in previous work. The orbits of test bodies and the precession of gyroscopes in this spacetime are calculated, leading to new constraints on the Chern-Simons parameter space due to current satellite experiments.Comment: 9 pages, 2 figures; minor corrections made; to appear in PR

Learning a second language via print: on the logical necessity of a fluent first language

How Deaf children should be taught to read has long been debated. Severely or profoundly Deaf children, who face challenges in acquiring language from its spoken forms, must learn to read a language they do not speak. We refer to this as learning a language via print. How children can learn language via print is not a topic regularly studied by educators, psychologists, or language acquisition theorists. Nonetheless, Deaf children can do this. We discuss how Deaf children can learn a written language via print by mapping print words and phrases to sign language sequences. However, established, time-tested curricula for using a signed language to teach the print forms of spoken languages do not exist. We describe general principles for approaching this task, how it differs from acquiring a spoken language naturalistically, and empirical evidence that Deaf children's knowledge of a signed language facilitates and advances learning a printed language.nstitute of Education Sciences (US Dept of Education) Education Research Training Granthttps://doi.org/10.3389/fcomm.2022.900399Published versio

Hybrid Probabilistic Trajectory Optimization Using Null-Space Exploration

In the context of learning from demonstration, human examples are usually imitated in either Cartesian or joint space. However, this treatment might result in undesired movement trajectories in either space. This is particularly important for motion skills such as striking, which typically imposes motion constraints in both spaces. In order to address this issue, we consider a probabilistic formulation of dynamic movement primitives, and apply it to adapt trajectories in Cartesian and joint spaces simultaneously. The probabilistic treatment allows the robot to capture the variability of multiple demonstrations and facilitates the mixture of trajectory constraints from both spaces. In addition to this proposed hybrid space learning, the robot often needs to consider additional constraints such as motion smoothness and joint limits. On the basis of Jacobian-based inverse kinematics, we propose to exploit robot null-space so as to unify trajectory constraints from Cartesian and joint spaces while satisfying additional constraints. Evaluations of hand-shaking and striking tasks carried out with a humanoid robot demonstrate the applicability of our approach

Kernelized movement primitives

Imitation learning has been studied widely as a convenient way to transfer human skills to robots. This learning approach is aimed at extracting relevant motion patterns from human demonstrations and subsequently applying these patterns to different situations. Despite the many advancements that have been achieved, solutions for coping with unpredicted situations (e.g., obstacles and external perturbations) and high-dimensional inputs are still largely absent. In this paper, we propose a novel kernelized movement primitive (KMP), which allows the robot to adapt the learned motor skills and fulfill a variety of additional constraints arising over the course of a task. Specifically, KMP is capable of learning trajectories associated with high-dimensional inputs owing to the kernel treatment, which in turn renders a model with fewer open parameters in contrast to methods that rely on basis functions. Moreover, we extend our approach by exploiting local trajectory representations in different coordinate systems that describe the task at hand, endowing KMP with reliable extrapolation capabilities in broader domains. We apply KMP to the learning of time-driven trajectories as a special case, where a compact parametric representation describing a trajectory and its first-order derivative is utilized. In order to verify the effectiveness of our method, several examples of trajectory modulations and extrapolations associated with time inputs, as well as trajectory adaptations with high-dimensional inputs are provided