Extending the Nested Parallel Model to the Nested Dataflow Model with Provably Efficient Schedulers

The nested parallel (a.k.a. fork-join) model is widely used for writing parallel programs. However, the two composition constructs, i.e. "$\parallel$" (parallel) and "$;$" (serial), are insufficient in expressing "partial dependencies" or "partial parallelism" in a program. We propose a new dataflow composition construct "$\leadsto$" to express partial dependencies in algorithms in a processor- and cache-oblivious way, thus extending the Nested Parallel (NP) model to the \emph{Nested Dataflow} (ND) model. We redesign several divide-and-conquer algorithms ranging from dense linear algebra to dynamic-programming in the ND model and prove that they all have optimal span while retaining optimal cache complexity. We propose the design of runtime schedulers that map ND programs to multicore processors with multiple levels of possibly shared caches (i.e, Parallel Memory Hierarchies) and provide theoretical guarantees on their ability to preserve locality and load balance. For this, we adapt space-bounded (SB) schedulers for the ND model. We show that our algorithms have increased "parallelizability" in the ND model, and that SB schedulers can use the extra parallelizability to achieve asymptotically optimal bounds on cache misses and running time on a greater number of processors than in the NP model. The running time for the algorithms in this paper is $O\left(\frac{\sum_{i=0}^{h-1} Q^{*}({\mathsf t};\sigma\cdot M_i)\cdot C_i}{p}\right)$, where $Q^{*}$ is the cache complexity of task ${\mathsf t}$, $C_i$ is the cost of cache miss at level-$i$ cache which is of size $M_i$, $\sigma\in(0,1)$ is a constant, and $p$ is the number of processors in an $h$-level cache hierarchy

A Cardinality Minimization Approach to Security-Constrained Economic Dispatch

We present a threshold-based cardinality minimization formulation to model the security-constrained economic dispatch problem. The model aims to minimize the operating cost of the system while simultaneously reducing the number of lines operating in emergency operating zones during contingency events. The model allows the system operator to monitor the duration for which lines operate in emergency zones and ensure that they are within the acceptable reliability standards determined by the system operators. We develop a continuous difference-of-convex approximation of the cardinality minimization problem and a solution method to solve the problem. Our numerical experiments demonstrate that the cardinality minimization approach reduces the overall system operating cost as well as avoids prolonged periods of high electricity prices during contingency events

Hierarchical Graph Modeling for Multi-Scale Optimization of Power Systems

Hierarchical optimization architectures are used in power systems to manage disturbances and phenomena that arise at multiple spatial and temporal scales. We present a graph modeling abstraction for representing such architectures and an implementation in the ${\tt Julia}$ package ${\tt Plasmo.jl}$. We apply this framework to a tri-level hierarchical framework arising in wholesale market operations that involves day-ahead unit commitment, short-term unit commitment, and economic dispatch. We show that graph abstractions facilitate the construction, visualization, and solution of these complex problems.Comment: 7 pages, 9 figure

Motion correction for phase-resolved dynamic optical coherence tomography imaging of rodent cerebral cortex

Cardiac and respiratory motions in animals are the primary source of image quality degradation in dynamic imaging studies, especially when using phase-resolved imaging modalities such as spectral-domain optical coherence tomography (SD-OCT), whose phase signal is very sensitive to movements of the sample. This study demonstrates a method with which to compensate for motion artifacts in dynamic SD-OCT imaging of the rodent cerebral cortex. We observed that respiratory and cardiac motions mainly caused, respectively, bulk image shifts (BISs) and global phase fluctuations (GPFs). A cross-correlation maximization-based shift correction algorithm was effective in suppressing BISs, while GPFs were significantly reduced by removing axial and lateral global phase variations. In addition, a non-origin-centered GPF correction algorithm was examined. Several combinations of these algorithms were tested to find an optimized approach that improved image stability from 0.5 to 0.8 in terms of the cross-correlation over 4 s of dynamic imaging, and reduced phase noise by two orders of magnitude in ~8% voxels.K99 NS067050 - NINDS NIH HHS; R01EB000790 - NIBIB NIH HHS; R01 EB001954 - NIBIB NIH HHS; R01 EB001954-09 - NIBIB NIH HHS; P01NS055104 - NINDS NIH HHS; R01 NS057476 - NINDS NIH HHS; K99NS067050 - NINDS NIH HHS; R01 EB000790 - NIBIB NIH HHS; R01-EB001954 - NIBIB NIH HHS; R01NS057476 - NINDS NIH HHS; P01 NS055104 - NINDS NIH HHS; P41 EB015896 - NIBIB NIH HHSPublished versio

Neurological Manifestations of IgG4-Related Disease

IgG4-related disease (IgG4-RD) is a multisystem inflammatory disorder. Early recognition of IgG4-RD is important to avoid permanent organ dysfunction and disability. Neurological involvement by IgG4-RD is relatively uncommon, but well recognised—hypertrophic pachymeningitis and hypophysitis are the most frequent manifestations. Although the nervous system may be involved in isolation, this more frequently occurs in conjunction with involvement of other systems. Elevated circulating levels of IgG4 are suggestive of the condition, but these are not pathognomonic and exclusion of other inflammatory disorders including vasculitis is required. Wherever possible, a tissue diagnosis should be established. The characteristic histopathological changes include a lymphoplasmacytoid infiltrate, storiform fibrosis and obliterative phlebitis. IgG4-RD typically responds well to treatment with glucocorticoids, although relapse is relatively common and treatment with a steroid-sparing agent or rituximab may be required. Improved understanding of the pathogenesis of IgG4-RD is likely to lead to the development of more specific disease treatments in the future

A Review of Photovoltaic Thermal (PVT) technology for residential applications: performance indicators, progress, and opportunities

Solar energy has been one of the accessible and affordable renewable energy technologies for the last few decades. Photovoltaics and solar thermal collectors are mature technologies to harness solar energy. However, the efficiency of photovoltaics decays at increased operating temperatures, and solar thermal collectors suffer from low exergy. Furthermore, along with several financial, structural, technical and socio-cultural barriers, the limited shadow-free space on building rooftops has significantly affected the adoption of solar energy. Thus, Photovoltaic Thermal (PVT) collectors that combine the advantages of photovoltaic cells and solar thermal collector into a single system have been developed. This study gives an extensive review of different PVT systems for residential applications, their performance indicators, progress, limitations and research opportunities. The literature review indicated that PVT systems used air, water, bi-fluids, nanofluids, refrigerants and phase-change material as the cooling medium and are sometimes integrated with heat pumps and seasonal energy storage. The overall efficiency of a PVT system reached up to 81% depending upon the system design and environmental conditions, and there is generally a trade-off between thermal and electrical efficiency. The review also highlights future research prospects in areas such as materials for PVT collector design, long-term reliability experiments, multi-objective design optimisation, techno-exergo-economics and photovoltaic recycling

A multi-label, dual-output deep neural network for automated bug triaging

Bug tracking enables the monitoring and resolution of issues and bugs within organizations. Bug triaging, or assigning bugs to the owner(s) who will resolve them, is a critical component of this process because there are many incorrect assignments that waste developer time and reduce bug resolution throughput. In this work, we explore the use of a novel two-output deep neural network architecture (Dual DNN) for triaging a bug to both an individual team and developer, simultaneously. Dual DNN leverages this simultaneous prediction by exploiting its own guess of the team classes to aid in developer assignment. A multi-label classification approach is used for each of the two outputs to learn from all interim owners, not just the last one who closed the bug. We make use of a heuristic combination of the interim owners (owner-importance-weighted labeling) which is converted into a probability mass function (pmf). We employ a two-stage learning scheme, whereby the team portion of the model is trained first and then held static to train the team--developer and bug--developer relationships. The scheme employed to encode the team--developer relationships is based on an organizational chart (org chart), which renders the model robust to organizational changes as it can adapt to role changes within an organization. There is an observed average lift (with respect to both team and developer assignment) of 13%-points in 11-fold incremental-learning cross-validation (IL-CV) accuracy for Dual DNN utilizing owner-weighted labels compared with the traditional multi-class classification approach. Furthermore, Dual DNN with owner-weighted labels achieves average 11-fold IL-CV accuracies of 76% (team assignment) and 55% (developer assignment), outperforming reference models by 14%- and 25%-points, respectively, on a proprietary dataset with 236,865 entries.Comment: 8 pages, 2 figures, 9 table