Publications

The mixed-mode delaminating beam (MMDB) is a widely used test geometry designed to measure the fracture resistance of bimaterial interfaces under mixed-mode loading conditions. In the present work, linear-elastic finite element analyses are employed to determine the complex stress intensity factor, K, for an interfacial crack in this sample; results are found to confirm those of previous studies of the bilayer specimen. However, the numerical results further reveal that the region of K-dominance near the crack tip is very limited, extending merely ∼ 1/100 to 1/1000 of the sample height, about an order of magnitude smaller than for other common fracture-mechanics test samples. Analyses performed for this specimen geometry modified to include a thin sandwiched interlayer also indicate a very limited region of K-dominance, for example, extending ∼ 1/10 of the height of the sandwiched layer from the crack tip for very thin sandwiched layers. For the sandwiched geometry, two situations leading to a useful small scale yielding condition are described. Examples of the use of this specimen in the evaluation of the fracture toughness of ceramic joints are cited from the literature and comparisons are made between the size of the K-dominant region and the extent of crack tip plasticity. Based on these comparisons, the geometry-independent predictive power of linear-elastic fracture mechanics for common bimaterial couples and specimen dimensions using this geometry is challenged.

Abstract A high degree of uncertainty persists regarding current and future emissions of methane from both natural and constructed wetlands. Part of the problem is the existence of ‘hot spots’ of methane flux, which have not been clearly identified and studied at multiple scales. Methane has a short lifetime compared to carbon dioxide; thus, efforts to avoid methane hot spots from constructed wetlands can promptly decelerate the rate of atmospheric warming. In this study we measured methane fluxes using flux towers in a restored oligohaline wetland in the Sacramento–San Joaquin River Delta, where we previously identified a hot spot of methane flux using footprint-weighed flux maps and chambers. Our main objectives with this study were to determine why this hot spot occurs and what are the biogeochemical and microbiological conditions that lead to these high methane fluxes. We found four main mechanisms that explain the existence of the hot spot. (1) The hot spot was associated with areas where the water level was closer to the surface (2) Methane originated mostly from older unoxidized peat in deeper layers, which had a shorter migration pathway to the atmosphere at the hot spot location due to soil disturbance during wetland construction. (3) Relatively lower methane oxidation in the hot spot in the upper soil layer (10–30 cm under the surface), deduced from isotopic profiles in porewater carbon and upper-level methanotroph abundance. (4) Higher ebullition events at the hot spot that can be related to low water levels and lower bulk density throughout the soil profile. This study thus suggests that mitigating soil disturbances during wetland construction and managing water level can reduce the occurrence and magnitude of hot spots of methane flux in constructed wetlands.

The success of MapReduce has sparked the development of a diverse array of cluster computing frameworks. We believe that no single framework will be optimal for all applications, and that organizations will instead want to run multiple frameworks in the same cluster. Furthermore, to ease development of new frameworks, it is critical to identify common abstractions and modularize their architectures. To achieve these goals, we propose Nexus, a low-level substrate that provides isolation and efficient resource sharing across frameworks running on the same cluster, while giving each framework maximum control over the scheduling and execution of its jobs. Nexus fosters innovation in the cloud by letting organizations run new frameworks alongside existing ones and by letting framework developers focus on specific applications rather than building one-size-fits-all frameworks.

Scalable distributed dataflow systems have recently experienced widespread adoption, with commodity dataflow engines such as Hadoop and Spark, and even commodity SQL engines routinely supporting increasingly sophisticated analytics tasks (e.g., support vector machines, logistic regression, collaborative filtering). However, these systems' synchronous (often Bulk Synchronous Parallel) dataflow execution model is at odds with an increasingly important trend in the machine learning community: the use of asynchrony via shared, mutable state (i.e., data races) in convex programming tasks, which has---in a single-node context---delivered noteworthy empirical performance gains and inspired new research into asynchronous algorithms. In this work, we attempt to bridge this gap by evaluating the use of lightweight, asynchronous state transfer within a commodity dataflow engine. Specifically, we investigate the use of asynchronous sideways information passing (ASIP) that presents single-stage parallel iterators with a Volcano-like intra-operator iterator that can be used for asynchronous information passing. We port two synchronous convex programming algorithms, stochastic gradient descent and the alternating direction method of multipliers (ADMM), to use ASIPs. We evaluate an implementation of ASIPs within on Apache Spark that exhibits considerable speedups as well as a rich set of performance trade-offs in the use of these asynchronous algorithms.

While there have been significant gains in the field of automated video description, the generalization performance of automated description models to novel domains remains a major barrier to using these systems in the real world. Most visual description methods are known to capture and exploit patterns in the training data leading to evaluation metric increases, but what are those patterns? In this work, we examine several popular visual description datasets, and capture, analyze, and understand the dataset-specific linguistic patterns that models exploit but do not generalize to new domains. At the token level, sample level, and dataset level, we find that caption diversity is a major driving factor behind the generation of generic and uninformative captions. We further show that state-of-the-art models even outperform held-out ground truth captions on modern metrics, and that this effect is an artifact of linguistic diversity in datasets. Understanding this linguistic diversity is key to building strong captioning models, we recommend several methods and approaches for maintaining diversity in the collection of new data, and dealing with the consequences of limited diversity when using current models and metrics.

Nanostructured metals are usually strong because the ultrahigh density of internal boundaries restricts the mean free path of dislocations. Usually, they are also more brittle because of their diminished work-hardening ability. Nanotwinned materials, with coherent interfaces of mirror symmetry, can overcome this inherent trade-off. We show a bulk nanostructuring method that produces a multiscale, hierarchical twin architecture in a hexagonal closed-packed, solute-free, and coarse-grained titanium (Ti), with a substantial enhancement of tensile strength and ductility. Pure Ti achieved an ultimate tensile strength of almost 2 gigapascals and a true failure strain close to 100% at 77 kelvin. The multiscale twin structures are thermally stable up to 873 kelvin, which is above the critical temperature for many applications in extreme environments. Our results demonstrate a practical route to achieve attractive mechanical properties in Ti without involving exotic and often expensive alloying elements.

The WW-PLEKHA proteins PLEKHA5, PLEKHA6, and PLEKHA7 coordinate together with PDZD11 the anterograde traffic of the copper pump ATP7A from the trans-Golgi network to the cell periphery. WW-PLEKHAs promote PDZD11 interaction with the C-terminus of ATP7A and are required for maintaining low intracellular copper levels when cells face elevated copper.