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

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

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

Will a New Input Strategy Be Warranted in Non-Irrigated Corn Production under Global Climate Change? An Analysis Using Corn Production in the Midwestern United States

Incentives to delay agricultural inputs to increase output and protect against climate change and weather related risk are explored. When producer decision making in non-irrigated production in an environment of weather uncertainty is outlined an incentive is found when imperfect information and risk is present. The EPIC crop simulator is then used to assess the yield impact of fertilizer delaying in the non-irrigated corn component of cornsoybean rotations in Humboldt and Webster Counties, Iowa. Assessed in conjunction with disaster frequency data, crop simulations suggest optimal input timing to be six weeks or more after planting. Additional production considerations such as spring field conditions and fertilizer runoff also support a six week or later input delay strategy. This result held under both recent weather conditions and climate change projections through 2040. The strength of each component’s influence on the input delaying decision changes however. Disaster frequency, field conditions, and runoff considerations all increase the incentive to delay fertilizer inputs while the risk associated with yield loss from delaying fertilizer beyond the six week mark also increases while the increase in yield benefits decreases relative to recent conditions. Methods to incentivize adoption of delayed input practices are briefly outlined such as modification of the farm bill’s multi-peril crop insurance program while being reserved for greater discussion in later research on the soy side of corn-soybean rotations

Transversity and Collins functions from SIDIS and e+e- data

A global analysis of the experimental data on azimuthal asymmetries in semi-inclusive deep inelastic scattering (SIDIS), from the HERMES and COMPASS Collaborations, and in e+e- --> h1 h2 X processes, from the BELLE Collaboration, is performed. It results in the extraction of the Collins fragmentation function and, for the first time, of the transversity distribution function for u and d quarks. These turn out to have opposite signs and to be sizably smaller than their positivity bounds. Predictions for the azimuthal asymmetry A_{UT}^{sin(phi_h + phi_S)}, as will soon be measured at JLab and COMPASS operating on a transversely polarized proton target, are then presented.Comment: Revised version to appear in Phys. Rev. D. Few misprints corrected, new figure

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