Finding Patterns in a Knowledge Base using Keywords to Compose Table Answers

We aim to provide table answers to keyword queries against knowledge bases. For queries referring to multiple entities, like "Washington cities population" and "Mel Gibson movies", it is better to represent each relevant answer as a table which aggregates a set of entities or entity-joins within the same table scheme or pattern. In this paper, we study how to find highly relevant patterns in a knowledge base for user-given keyword queries to compose table answers. A knowledge base can be modeled as a directed graph called knowledge graph, where nodes represent entities in the knowledge base and edges represent the relationships among them. Each node/edge is labeled with type and text. A pattern is an aggregation of subtrees which contain all keywords in the texts and have the same structure and types on node/edges. We propose efficient algorithms to find patterns that are relevant to the query for a class of scoring functions. We show the hardness of the problem in theory, and propose path-based indexes that are affordable in memory. Two query-processing algorithms are proposed: one is fast in practice for small queries (with small patterns as answers) by utilizing the indexes; and the other one is better in theory, with running time linear in the sizes of indexes and answers, which can handle large queries better. We also conduct extensive experimental study to compare our approaches with a naive adaption of known techniques.Comment: VLDB 201

Authentic Learning Design Failures: The Need for Learner and Contextual Analysis and Participatory Design

This design case examines what program leaders learned from failures in the design of a program of authentic learning about teaching diverse audiences through educational outreach. The program was initiated and then redesigned to develop the teaching and communication skills of graduate students from a wide range of backgrounds by engaging them in authentic experiences with middle school teachers and students. Analysis of post-program data revealed seven design failures related to the lack of upfront analysis to inform the program design. Each design failure was detailed through a fishbone diagram method to identify the corre- sponding contributing factors. The failures in this design case reinforce the need for upfront learner analysis and contextual analysis. A participatory design was also suggested from the post-program data analysis. An instructional design model was recommended for continuous program redesign

Network Inference via the Time-Varying Graphical Lasso

Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements. In order to spot trends, detect anomalies, and interpret the temporal dynamics of such data, it is essential to understand the relationships between the different entities and how these relationships evolve over time. In this paper, we introduce the time-varying graphical lasso (TVGL), a method of inferring time-varying networks from raw time series data. We cast the problem in terms of estimating a sparse time-varying inverse covariance matrix, which reveals a dynamic network of interdependencies between the entities. Since dynamic network inference is a computationally expensive task, we derive a scalable message-passing algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in an efficient way. We also discuss several extensions, including a streaming algorithm to update the model and incorporate new observations in real time. Finally, we evaluate our TVGL algorithm on both real and synthetic datasets, obtaining interpretable results and outperforming state-of-the-art baselines in terms of both accuracy and scalability

A radio structure resolved at the deca-parsec scale in radio-quiet quasar PDS 456 with an extremely powerful X-ray outflow

Active galactic nuclei (AGN) accreting at rates close to the Eddington limit can host radiatively driven mildly relativistic outflows. Some of these X-ray absorbing but powerful outflows may produce strong shocks resulting in a significant non-thermal emission. This outflow-driven radio emission may be detectable in the radio-quiet quasar PDS 456 since it has a bolometric luminosity reaching the Eddington limit and a relativistic wide-aperture X-ray outflow with a kinetic power high enough to quench the star formation in its host galaxy. To investigate this possibility, we performed very-long-baseline interferometric (VLBI) observations of the quasar with the European VLBI Network (EVN) at 5 GHz. The EVN image with the full resolution reveals two faint and diffuse radio components with a projected separation of about 20 pc and an average brightness temperature of around two million Kelvin. In relation to the optical sub-mas-accuracy position measured by the Gaia mission, the two components are very likely on opposite sides of an undetected radio core. The VLBI structure at the deca-pc scale can thus be either a young jet or a bidirectional radio-emitting outflow, launched in the vicinity of a strongly accreting central engine. Two diffuse components at the hecto-pc scale, likely the relic radio emission from the past AGN activity, are tentatively detected on each side in the low-resolution EVN image.Comment: 6 pages, 2 figures, 1 table. Accepted for publication in MNRA

An optimal transport approach for the multiple quantile hedging problem

We consider the multiple quantile hedging problem, which is a class of partial hedging problems containing as special examples the quantile hedging problem (F{\"o}llmer \& Leukert 1999) and the PnL matching problem (introduced in Bouchard \& Vu 2012). In complete non-linear markets, we show that the problem can be reformulated as a kind of Monge optimal transport problem. Using this observation, we introduce a Kantorovitch version of the problem and prove that the value of both problems coincide. In the linear case, we thus obtain that the multiple quantile hedging problem can be seen as a semi-discrete optimal transport problem, for which we further introduce the dual problem. We then prove that there is no duality gap, allowing us to design a numerical method based on SGA algorithms to compute the multiple quantile hedging price

Imaging and variability studies of CTA~102 during the 2016 January γ\gamma-ray flare

The $\gamma$-ray bright blazar CTA 102 is studied using imaging (new 15 GHz and archival 43 GHz Very Long Baseline Array, VLBA data) and time variable optical flux density, polarization degree and electric vector position angle (EVPA) spanning between 2015 June 1 and 2016 October 1, covering a prominent $\gamma$-ray flare during 2016 January. The pc-scale jet indicates expansion with oscillatory features upto 17 mas. Component proper motions are in the range 0.04 - 0.33 mas/yr with acceleration upto 1.2 mas followed by a slowing down beyond 1.5 mas. A jet bulk Lorentz factor $\geq$ 17.5, position angle of 128.3 degrees, inclination angle $\leq$ 6.6 degrees and intrinsic half opening angle $\leq$ 1.8 degrees are derived from the VLBA data. These inferences are employed in a helical jet model to infer long term variability in flux density, polarization degree, EVPA and a rotation of the Stokes Q and U parameters. A core distance of $r_{\rm core,43 \ GHz}$ = 22.9 pc, and a magnetic field strength at 1 pc and the core location of 1.57 G and 0.07 G respectively are inferred using the core shift method. The study is useful in the context of estimating jet parameters and in offering clues to distinguish mechanisms responsible for variability over different timescales.Comment: 20 pages, 7 figures, 3 tables; accepted for publication in Ap