Revealing the unseen: how to expose cloud usage while protecting user privacy

Cloud users have little visibility into the performance characteristics and utilization of the physical machines underpinning the virtualized cloud resources they use. This uncertainty forces users and researchers to reverse engineer the inner workings of cloud systems in order to understand and optimize the conditions their applications operate. At Massachusetts Open Cloud (MOC), as a public cloud operator, we'd like to expose the utilization of our physical infrastructure to stop this wasteful effort. Mindful that such exposure can be used maliciously for gaining insight into other user's workloads, in this position paper we argue for the need for an approach that balances openness of the cloud overall with privacy for each tenant inside of it. We believe that this approach can be instantiated via a novel combination of several security and privacy technologies. We discuss the potential benefits, implications of transparency for cloud systems and users, and technical challenges/possibilities.Accepted manuscrip

Small Engine Component Technology (SECT)

A study of small gas turbine engines was conducted to identify high payoff technologies for year-2000 engines and to define companion technology plans. The study addressed engines in the 186 to 746 KW (250 to 1000 shp) or equivalent thrust range for rotorcraft, commuter (turboprop), cruise missile (turbojet), and APU applications. The results show that aggressive advancement of high payoff technologies can produce significant benefits, including reduced SFC, weight, and cost for year-2000 engines. Mission studies for these engines show potential fuel burn reductions of 22 to 71 percent. These engine benefits translate into reductions in rotorcraft and commuter aircraft direct operating costs (DOC) of 7 to 11 percent, and in APU-related DOCs of 37 to 47 percent. The study further shows that cruise missile range can be increased by as much as 200 percent (320 percent with slurry fuels) for a year-2000 missile-turbojet system compared to a current rocket-powered system. The high payoff technologies were identified and the benefits quantified. Based on this, technology plans were defined for each of the four engine applications as recommended guidelines for further NASA research and technology efforts to establish technological readiness for the year 2000

Deep Haptic Model Predictive Control for Robot-Assisted Dressing

Robot-assisted dressing offers an opportunity to benefit the lives of many people with disabilities, such as some older adults. However, robots currently lack common sense about the physical implications of their actions on people. The physical implications of dressing are complicated by non-rigid garments, which can result in a robot indirectly applying high forces to a person's body. We present a deep recurrent model that, when given a proposed action by the robot, predicts the forces a garment will apply to a person's body. We also show that a robot can provide better dressing assistance by using this model with model predictive control. The predictions made by our model only use haptic and kinematic observations from the robot's end effector, which are readily attainable. Collecting training data from real world physical human-robot interaction can be time consuming, costly, and put people at risk. Instead, we train our predictive model using data collected in an entirely self-supervised fashion from a physics-based simulation. We evaluated our approach with a PR2 robot that attempted to pull a hospital gown onto the arms of 10 human participants. With a 0.2s prediction horizon, our controller succeeded at high rates and lowered applied force while navigating the garment around a persons fist and elbow without getting caught. Shorter prediction horizons resulted in significantly reduced performance with the sleeve catching on the participants' fists and elbows, demonstrating the value of our model's predictions. These behaviors of mitigating catches emerged from our deep predictive model and the controller objective function, which primarily penalizes high forces.Comment: 8 pages, 12 figures, 1 table, 2018 IEEE International Conference on Robotics and Automation (ICRA

Fractal Heterogeneous Media

A method is proposed for generating compact fractal disordered media, by generalizing the random midpoint displacement algorithm. The obtained structures are invasive stochastic fractals, with the Hurst exponent varying as a continuous parameter, as opposed to lacunar deterministic fractals, such as the Menger sponge. By employing the Detrending Moving Average algorithm [Phys. Rev. E 76, 056703 (2007)], the Hurst exponent of the generated structure can be subsequently checked. The fractality of such a structure is referred to a property defined over a three dimensional topology rather than to the topology itself. Consequently, in this framework, the Hurst exponent should be intended as an estimator of compactness rather than of roughness. Applications can be envisaged for simulating and quantifying complex systems characterized by self-similar heterogeneity across space. For example, exploitation areas range from the design and control of multifunctional self-assembled artificial nano and micro structures, to the analysis and modelling of complex pattern formation in biology, environmental sciences, geomorphological sciences, etc

Pomelo, a tool for computing Generic Set Voronoi Diagrams of Aspherical Particles of Arbitrary Shape

We describe the development of a new software tool, called "Pomelo", for the calculation of Set Voronoi diagrams. Voronoi diagrams are a spatial partition of the space around the particles into separate Voronoi cells, e.g. applicable to granular materials. A generalization of the conventional Voronoi diagram for points or monodisperse spheres is the Set Voronoi diagram, also known as navigational map or tessellation by zone of influence. In this construction, a Set Voronoi cell contains the volume that is closer to the surface of one particle than to the surface of any other particle. This is required for aspherical or polydisperse systems. Pomelo is designed to be easy to use and as generic as possible. It directly supports common particle shapes and offers a generic mode, which allows to deal with any type of particles that can be described mathematically. Pomelo can create output in different standard formats, which allows direct visualization and further processing. Finally, we describe three applications of the Set Voronoi code in granular and soft matter physics, namely the problem of packings of ellipsoidal particles with varying degrees of particle-particle friction, mechanical stable packings of tetrahedra and a model for liquid crystal systems of particles with shapes reminiscent of pearsComment: 4 pages, 9 figures, Submitted to Powders and Grains 201

The complexity that the first stars brought to the Universe: Fragility of metal enriched gas in a radiation field

The initial mass function (IMF) of the first (Population III) stars and Population II (Pop II) stars is poorly known due to a lack of observations of the period between recombination and reionization. In simulations of the formation of the first stars, it has been shown that, due to the limited ability of metal-free primordial gas to cool, the IMF of the first stars is a few orders of magnitude more massive than the current IMF. The transition from a high-mass IMF of the first stars to a lower-mass current IMF is thus important to understand. To study the underlying physics of this transition, we performed several simulations using the cosmological hydrodynamic adaptive mesh refinement code Enzo for metallicities of 10^{-4}, 10^{-3}, 10^{-2}, and 10^{-1} Z_{\odot}. In our simulations we include a star formation prescription that is derived from a metallicity dependent multi-phase ISM structure, an external UV radiation field, and a mechanical feedback algorithm. We also implement cosmic ray heating, photoelectric heating and gas-dust heating/cooling, and follow the metal enrichment of the ISM. It is found that the interplay between metallicity and UV radiation leads to the co-existence of Pop III and Pop II star formation in non-zero metallicity (Z/Z_{\odot} \geq10^{-2}) gas. A cold (T10^{-22} g cm^{-3}) gas phase is fragile to ambient UV radiation. In a metal-poor (Z/Z_{\odot} \leq10^{-3}) gas, the cold and dense gas phase does not form in the presence of a radiation field of F_{0}\sim10^{-5}-10^{-4} erg cm^{-2} s^{-1}. Therefore, metallicity by itself is not a good indicator of the Pop III-Pop II transition. Metal-rich (Z/Z_{\odot}\geq10^{-2}) gas dynamically evolves two to three orders of magnitude faster than metal poor gas (Z/Z_{\odot}\leq10^{-3}). The simulations including SNe show that pre-enrichment of the halo does not affect the mixing of metals.Comment: Published in Ap

Machine Learning and Cosmological Simulations II: Hydrodynamical Simulations

We extend a machine learning (ML) framework presented previously to model galaxy formation and evolution in a hierarchical universe using N-body + hydrodynamical simulations. In this work, we show that ML is a promising technique to study galaxy formation in the backdrop of a hydrodynamical simulation. We use the Illustris Simulation to train and test various sophisticated machine learning algorithms. By using only essential dark matter halo physical properties and no merger history, our model predicts the gas mass, stellar mass, black hole mass, star formation rate, $g-r$ color, and stellar metallicity fairly robustly. Our results provide a unique and powerful phenomenological framework to explore the galaxy-halo connection that is built upon a solid hydrodynamical simulation. The promising reproduction of the listed galaxy properties demonstrably place ML as a promising and a significantly more computationally efficient tool to study small-scale structure formation. We find that ML mimics a full-blown hydrodynamical simulation surprisingly well in a computation time of mere minutes. The population of galaxies simulated by ML, while not numerically identical to Illustris, is statistically and physically robust and follows the same fundamental observational constraints. Machine learning offers an intriguing and promising technique to create quick mock galaxy catalogs in the future.Comment: 20 pages, 27 figures, 6 tables. Accepted to MNRA