Post-Impact Thermal Evolution of Porous Planetesimals

Impacts between planetesimals have largely been ruled out as a heat source in the early Solar System, by calculations that show them to be an inefficient heat source and unlikely to cause global heating. However, the long-term, localized thermal effects of impacts on planetesimals have never been fully quantified. Here, we simulate a range of impact scenarios between planetesimals to determine the post-impact thermal histories of the parent bodies, and hence the importance of impact heating in the thermal evolution of planetesimals. We find on a local scale that heating material to petrologic type 6 is achievable for a range of impact velocities and initial porosities, and impact melting is possible in porous material at a velocity of > 4 km/s. Burial of heated impactor material beneath the impact crater is common, insulating that material and allowing the parent body to retain the heat for extended periods (~ millions of years). Cooling rates at 773 K are typically 1 - 1000 K/Ma, matching a wide range of measurements of metallographic cooling rates from chondritic materials. While the heating presented here is localized to the impact site, multiple impacts over the lifetime of a parent body are likely to have occurred. Moreover, as most meteorite samples are on the centimeter to meter scale, the localized effects of impact heating cannot be ignored.Comment: 38 pages, 9 figures, Revised for Geochimica et Cosmochimica Acta (Sorry, they do not accept LaTeX

Comparison of two sampling protocols and four home-range estimators using radio-tracking data from urban badgers Meles meles

Radio-telemetry is often the method of choice for studies of species whose behaviour is difficult to observe directly. However, considerable debate has ensued about the best way of deriving home-range estimates. In recent years, kernel estimators have become the most widely used method, together with the oldest and simplest method, the minimum convex polygon (MCP). More recently, it has been suggested that the local convex hull (LCH) might be more appropriate than kernel methods in cases where an animal’s home range includes a priori inaccessible areas. Yet another method, the Brownian bridge (BB), explicitly uses autocorrelated data to determine movement paths and, ultimately, home ranges or migration routes of animals. Whereas several studies have used simulation techniques to compare these different methods, few have used data from real animals. We used radio-telemetric data from urban badgers Meles meles to compare two sampling protocols (10-minute vs at least 30-minute inter-fix intervals) and four home-range estimators (MCP, fixed kernels (FK), LCH and BB). We used a multi-response permutation procedure and randomisation tests to compare overall patterns of fixes and degree of overlap of home ranges estimated using data from different sampling protocols, and a general linear model to compare the influence of sampling protocols and home-range estimator on the size of habitat patches. The shape of the estimated home ranges was influenced by sampling protocol in some cases. By contrast, the sizes and proportions of different habitats within home ranges were influenced by estimator type but not by sampling protocol. LCH performed consistently better than FK, and is especially appropriate for patchy study areas containing frequent no-go zones. However, we recommend using LCH in combination with other methods to estimate total range size, because LCH tended to produce smaller estimates than any other method. Results relating to BB are preliminary but suggest that this method is unsuitable for species in which range size is small compared to average travel speed.Marie-Curie Intra-European Fellowship (BSSUB - 24007); Defra WSC contract WM0304; Wildlife Biology granted the permit to upload the article to this repositor

Phase equilibrium modeling for high temperature metallization on GaAs solar cells

Recent trends in performance specifications and functional requirements have brought about the need for high temperature metallization technology to be developed for survivable DOD space systems and to enhance solar cell reliability. The temperature constitution phase diagrams of selected binary and ternary systems were reviewed to determine the temperature and type of phase transformation present in the alloy systems. Of paramount interest are the liquid-solid and solid-solid transformations. Data are being utilized to aid in the selection of electrical contact materials to gallium arsenide solar cells. Published data on the phase diagrams for binary systems is readily available. However, information for ternary systems is limited. A computer model is being developed which will enable the phase equilibrium predictions for ternary systems where experimental data is lacking

Focussing quantum states

Does the size of atoms present a lower limit to the size of electronic structures that can be fabricated in solids? This limit can be overcome by using devices that exploit quantum mechanical scattering of electron waves at atoms arranged in focussing geometries on selected surfaces. Calculations reveal that features smaller than a hydrogen atom can be obtained. These structures are potentially useful for device applications and offer a route to the fabrication of ultrafine and well defined tips for scanning tunneling microscopy.Comment: 4 pages, 4 figure

Calculation of Gallium-metal-Arsenic phase diagrams

Electrical contacts and metallization to GaAs solar cells must survive at high temperatures for several minutes under specific mission scenarios. The determination of which metallizations or alloy systems that are able to withstand extreme thermal excursions with minimum degradation to solar cell performance can be predicted by properly calculated temperature constitution phase diagrams. A method for calculating a ternary diagram and its three constituent binary phase diagrams is briefly outlined and ternary phase diagrams for three Ga-As-X alloy systems are presented. Free energy functions of the liquid and solid phase are approximated by the regular solution theory. Phase diagrams calculated using this method are presented for the Ga-As-Ge and Ga-As-Ag systems

Sim-to-real reinforcement learning for deformable object manipulation

We have seen much recent progress in rigid object manipulation, but in- teraction with deformable objects has notably lagged behind. Due to the large con- figuration space of deformable objects, solutions using traditional modelling ap- proaches require significant engineering work. Perhaps then, bypassing the need for explicit modelling and instead learning the control in an end-to-end manner serves as a better approach? Despite the growing interest in the use of end-to-end robot learning approaches, only a small amount of work has focused on their ap- plicability to deformable object manipulation. Moreover, due to the large amount of data needed to learn these end-to-end solutions, an emerging trend is to learn control policies in simulation and then transfer them over to the real world. To- date, no work has explored whether it is possible to learn and transfer deformable object policies. We believe that if sim-to-real methods are to be employed fur- ther, then it should be possible to learn to interact with a wide variety of objects, and not only rigid objects. In this work, we use a combination of state-of-the-art deep reinforcement learning algorithms to solve the problem of manipulating de- formable objects (specifically cloth). We evaluate our approach on three tasks — folding a towel up to a mark, folding a face towel diagonally, and draping a piece of cloth over a hanger. Our agents are fully trained in simulation with domain randomisation, and then successfully deployed in the real world without having seen any real deformable objects

Bioinformatics tools for analysing viral genomic data

The field of viral genomics and bioinformatics is experiencing a strong resurgence due to high-throughput sequencing (HTS) technology, which enables the rapid and cost-effective sequencing and subsequent assembly of large numbers of viral genomes. In addition, the unprecedented power of HTS technologies has enabled the analysis of intra-host viral diversity and quasispecies dynamics in relation to important biological questions on viral transmission, vaccine resistance and host jumping. HTS also enables the rapid identification of both known and potentially new viruses from field and clinical samples, thus adding new tools to the fields of viral discovery and metagenomics. Bioinformatics has been central to the rise of HTS applications because new algorithms and software tools are continually needed to process and analyse the large, complex datasets generated in this rapidly evolving area. In this paper, the authors give a brief overview of the main bioinformatics tools available for viral genomic research, with a particular emphasis on HTS technologies and their main applications. They summarise the major steps in various HTS analyses, starting with quality control of raw reads and encompassing activities ranging from consensus and de novo genome assembly to variant calling and metagenomics, as well as RNA sequencing

RIDI: Robust IMU Double Integration

This paper proposes a novel data-driven approach for inertial navigation, which learns to estimate trajectories of natural human motions just from an inertial measurement unit (IMU) in every smartphone. The key observation is that human motions are repetitive and consist of a few major modes (e.g., standing, walking, or turning). Our algorithm regresses a velocity vector from the history of linear accelerations and angular velocities, then corrects low-frequency bias in the linear accelerations, which are integrated twice to estimate positions. We have acquired training data with ground-truth motions across multiple human subjects and multiple phone placements (e.g., in a bag or a hand). The qualitatively and quantitatively evaluations have demonstrated that our algorithm has surprisingly shown comparable results to full Visual Inertial navigation. To our knowledge, this paper is the first to integrate sophisticated machine learning techniques with inertial navigation, potentially opening up a new line of research in the domain of data-driven inertial navigation. We will publicly share our code and data to facilitate further research

SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks

Ever more robust, accurate and detailed mapping using visual sensing has proven to be an enabling factor for mobile robots across a wide variety of applications. For the next level of robot intelligence and intuitive user interaction, maps need extend beyond geometry and appearence - they need to contain semantics. We address this challenge by combining Convolutional Neural Networks (CNNs) and a state of the art dense Simultaneous Localisation and Mapping (SLAM) system, ElasticFusion, which provides long-term dense correspondence between frames of indoor RGB-D video even during loopy scanning trajectories. These correspondences allow the CNN's semantic predictions from multiple view points to be probabilistically fused into a map. This not only produces a useful semantic 3D map, but we also show on the NYUv2 dataset that fusing multiple predictions leads to an improvement even in the 2D semantic labelling over baseline single frame predictions. We also show that for a smaller reconstruction dataset with larger variation in prediction viewpoint, the improvement over single frame segmentation increases. Our system is efficient enough to allow real-time interactive use at frame-rates of approximately 25Hz