Publications

High-carbon chromium martensite steels are commonly selected for bearing components in the power generation, automotive and aerospace industries where fatigue failure is a major concern. Accordingly, it is of importance to elucidate the structure-property relationships governing the fatigue properties of such bearing steels. Here, the role of microstructure in influencing the fatigue-crack propagation behavior of a high-carbon chromium SUJ2 bearing steel is examined with emphasis on three martensitic structures: (i) a fine-grained (∼10 μm) structure with intragranular (but no intergranular) carbides, (ii) a coarser-grained (∼25 μm) structure with discontinuous grain-boundary carbides primarily near triple junctions, and (iii) a coarse-grained (∼31 μm) structure with continuous grain-boundary carbides. Although growth rates were fairly similar above ∼10−8 m/cycle, significant differences in behavior were observed at lower, near-threshold growth rates; specifically at a load ratio of 0.1, the ΔK th fatigue threshold was increased from 3.8 MPa m½ in the fine-grained structure to 5.9 to 6.3 MPa m½ in the two coarser-grained structures. The dominant factor governing such near-threshold behavior was found to be crack-tip shielding from roughness-induced crack closure, which was most pronounced in the coarser-grained structures; additionally, shielding from crack deflection and branching was apparent in these structures as the crack propagated through the network of intersecting martensite lamellae within the coarse grains. Compared to high-strength stainless steels and low-strength carbon steels, the fine-grained SUJ2 steel displays an exceptional combination of high strength and fatigue-crack growth resistance and is considered to be a superior structural alloy for bearing applications.

Serverless cloud computing handles virtually all the system administration operations needed to make it easier for programmers to use the cloud. It provides an interface that greatly simplifies cloud programming, and represents an evolution that parallels the transition from assembly language to high-level programming languages. This paper gives a quick history of cloud computing, including an accounting of the predictions of the 2009 Berkeley View of Cloud Computing paper, explains the motivation for serverless computing, describes applications that stretch the current limits of serverless, and then lists obstacles and research opportunities required for serverless computing to fulfill its full potential. Just as the 2009 paper identified challenges for the cloud and predicted they would be addressed and that cloud use would accelerate, we predict these issues are solvable and that serverless computing will grow to dominate the future of cloud computing.

Semantic navigation is necessary to deploy mobile robots in uncontrolled environments like our homes, schools, and hospitals. Many learning-based approaches have been proposed in response to the lack of semantic understanding of the classical pipeline for spatial navigation, which builds a geometric map using depth sensors and plans to reach point goals. Broadly, end-to-end learning approaches reactively map sensor inputs to actions with deep neural networks, while modular learning approaches enrich the classical pipeline with learning-based semantic sensing and exploration. But learned visual navigation policies have predominantly been evaluated in simulation. How well do different classes of methods work on a robot? We present a large-scale empirical study of semantic visual navigation methods comparing representative methods from classical, modular, and end-to-end learning approaches across six homes with no prior experience, maps, or instrumentation. We find that modular learning works well in the real world, attaining a 90% success rate. In contrast, end-to-end learning does not, dropping from 77% simulation to 23% real-world success rate due to a large image domain gap between simulation and reality. For practitioners, we show that modular learning is a reliable approach to navigate to objects: modularity and abstraction in policy design enable Sim-to-Real transfer. For researchers, we identify two key issues that prevent today's simulators from being reliable evaluation benchmarks - (A) a large Sim-to-Real gap in images and (B) a disconnect between simulation and real-world error modes - and propose concrete steps forward.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Identifying next-generation batteries with multivalent ions, such as Ca²⁺ is an active area of research to meet the increasing demand for large-scale, renewable energy storage solutions. Despite the promise of higher energy densities with multivalent batteries, one of their main challenges is addressing the sluggish kinetics in cathodes that arise from stronger electrostatic interactions between the multivalent ion and host lattice. In this paper, zircons are theoretically and experimentally evaluated as Ca cathodes. A migration barrier as low as 113 meV is computationally found in YVO₄, which is the lowest Ca²⁺ barrier reported to date. Low barriers are confirmed across 18 zircon compositions, which are related to the low coordination change and reduced interstitial site preference of Ca²⁺ along the diffusion pathway. Among the four materials (BiVO₄, YVO₄, EuCrO₄, and YCrO₄) that were synthesized, characterized, and electrochemically cycled, the highest initial capacity of 81 mA h/g and the most reversible capacity of 65 mA h/g were achieved in YVO₄ and BiVO₄, respectively. Despite the facile migration of multivalent ions in zircons, density functional theory predictions of the unstable, discharged structures at higher Ca²⁺ concentrations (Ca_{<i>x</i>>0.25}ABO₄), the low dimensionality of the migration pathway, and the defect analysis of the B site atom can rationalize the limited intercalation observed upon electrochemical cycling.

Algorithms for generating multi-scale representations of gray-scale images are presented. A number of possible approaches are described to produce low-pass and band-pass decompositions using simple analogic algorithms. It is also shown how the wavelet transform can be approximated with a similar technique and be used to obtain multi-level descriptions of the input data. This paper presents some methods how the cellular neural network (CNN) Universal Machine can be used effectively for generating multi-scale representations of gray-scale imagery.