Publications

An increasing amount of mobile analytics is performed on data that is procured in a real-time fashion to make real-time decisions. Such tasks include simple reporting on streams to sophisticated model building. However, the practicality of these analyses are impeded in several domains because they are faced with a fundamental trade-off between data collection latency and analysis accuracy. In this paper, we first study this trade-off in the context of a specific domain, Cellular Radio Access Networks (RAN). We find that the trade-off can be resolved using two broad, general techniques: intelligent data grouping and task formulations that leverage domain characteristics. Based on this, we present CellScope, a system that applies a domain specific formulation and application of Multi-task Learning (MTL) to RAN performance analysis. It uses three techniques: feature engineering to transform raw data into effective features, a PCA inspired similarity metric to group data from geographically nearby base stations sharing performance commonalities, and a hybrid online-offline model for efficient model updates. Our evaluation shows that CellScope's accuracy improvements over direct application of ML range from 2.5× to 4.4× while reducing the model update overhead by up to 4.8×. We have also used CellScope to analyze an LTE network of over 2 million subscribers, where it reduced troubleshooting efforts by several magnitudes. We then apply the underlying techniques in CellScope to another domain specific problem, mobile phone energy bug diagnosis, and show that the techniques are general.

Recent years have witnessed a surge of interest in congestion control. Unfortunately, the overwhelmingly large design space along with the increasingly diverse range of application environments makes evaluating congestion control protocols a daunting task. Researchers often use simulation and experiments to examine the performance of designs in specific contexts, but this gives limited insight into the more general properties of these schemes and provides no information about the inherent limits of congestion control designs, e.g., which properties are simultaneously achievable. To complement simulation and experimentation, we advocate a principled framework for reasoning about congestion control protocols. We report on our initial steps in this direction, which was inspired by the axiomatic approach from social choice theory and game theory. We consider several natural requirements ("axioms") from congestion control protocols -- e.g., efficient resource-utilization, loss-avoidance, fairness, stability, and TCP-friendliness -- and investigate which combinations of these can be achieved within a single design. Thus, our framework allows us to investigate the fundamental tradeoffs between desiderata, and to identify where existing and new congestion control architectures fit within the space of possible outcomes. We believe that our results are but a first step in the axiomatic exploration of congestion control and leave the reader with exciting directions for future research.

A cobalt(ii) complex supported by the new tetradentate polypyridyl ligand PY4 is an electrocatalyst for the reduction of protons to hydrogen and can operate in 50% aqueous media.

ε-Caprolactam and δ-valerolactam are important commodity chemicals used in the manufacture of nylons, with millions of tons produced annually. Biological production of these highly valued chemicals has been limited due to a lack of enzymes that cyclize ω-amino fatty acid precursors to corresponding lactams under ambient conditions. In this study, we demonstrated production of these chemicals using ORF26, an acyl-CoA ligase involved in the biosynthesis of ECO-02301 in Streptomyces aizunensis. This enzyme has a broad substrate spectrum and can cyclize 4-aminobutyric acid into γ-butyrolactam, 5-aminovaleric acid into δ-valerolactam and 6-aminocaproic acid into ε-caprolactam. Recombinant E. coli expressing ORF26 produced valerolactam and caprolactam when 5-aminovaleric acid and 6-aminocaproic acid were added to the culture medium. Upon coexpressing ORF26 with a metabolic pathway that produced 5-aminovaleric acid from lysine, we were able to demonstrate production of δ-valerolactam from lysine.

Many modern network designs incorporate "failover" paths into routers' forwarding tables. We initiate the theoretical study of the conditions under which such resilient routing tables can guarantee delivery of packets.

Abstract Phase diagrams offer substantial predictive power for materials synthesis by identifying the stability regions of target phases. However, thermodynamic phase diagrams do not offer explicit information regarding the kinetic competitiveness of undesired by-product phases. Here we propose a quantitative and computable thermodynamic metric to identify synthesis conditions under which the propensity to form kinetically competing by-products is minimized. We hypothesize that thermodynamic competition is minimized when the difference in free energy between a target phase and the minimal energy of all other competing phases is maximized. We validate this hypothesis for aqueous materials synthesis through two empirical approaches: first, by analysing 331 aqueous synthesis recipes text-mined from the literature; and second, by systematic experimental synthesis of LiIn(IO 3 ) 4 and LiFePO 4 across a wide range of aqueous electrochemical conditions. Our results show that even for synthesis conditions that are within the stability region of a thermodynamic Pourbaix diagram, phase-pure synthesis occurs only when thermodynamic competition with undesired phases is minimized.

Despite remarkable progress in the field of MOFs, structures based on long-flexible organic linkers are scarce and the majority of such materials rely on rigid linkers. In this work, crystals of a new metal-organic double ladder (MODL) are obtained by linking a pentapeptide (NH2-Glu-pCO2Phe-pCO2Phe-Ala-Gly-OH) with cadmium acetate to produce a Cd(2-pyrrolidone-pCO2Phe-pCO2Phe-Ala-Gly)(H2O)3 framework. SEM and TEM analyses show the fibrous nature of the crystals and show that the infinite cadmium oxide rod secondary building units (SBUs) are aligned with the longitudinal axis of the nanofibers.

Abstract Patterns of coexpression can reveal networks of functionally related genes and provide deeper understanding of processes requiring multiple gene products. We performed an analysis of coexpression networks for 1,330 genes from the AraCyc database of metabolic pathways in Arabidopsis (Arabidopsis thaliana). We found that genes associated with the same metabolic pathway are, on average, more highly coexpressed than genes from different pathways. Positively coexpressed genes within the same pathway tend to cluster close together in the pathway structure, while negatively correlated genes typically occupy more distant positions. The distribution of coexpression links per gene is highly skewed, with a small but significant number of genes having numerous coexpression partners but most having fewer than 10. Genes with multiple connections (hubs) tend to be single-copy genes, while genes with multiple paralogs are coexpressed with fewer genes, on average, than single-copy genes, suggesting that the network expands through gene duplication, followed by weakening of coexpression links involving duplicate nodes. Using a network-analysis algorithm based on coexpression with multiple pathway members (pathway-level coexpression), we identified and prioritized novel candidate pathway members, regulators, and cross pathway transcriptional control points for over 140 metabolic pathways. To facilitate exploration and analysis of the results, we provide a Web site (http://www.transvar.org/at_coexpress/analysis/web) listing analyzed pathways with links to regression and pathway-level coexpression results. These methods and results will aid in the prioritization of candidates for genetic analysis of metabolism in plants and contribute to the improvement of functional annotation of the Arabidopsis genome.