A System for Real-Time Interactive Analysis of Deep Learning Training

Performing diagnosis or exploratory analysis during the training of deep learning models is challenging but often necessary for making a sequence of decisions guided by the incremental observations. Currently available systems for this purpose are limited to monitoring only the logged data that must be specified before the training process starts. Each time a new information is desired, a cycle of stop-change-restart is required in the training process. These limitations make interactive exploration and diagnosis tasks difficult, imposing long tedious iterations during the model development. We present a new system that enables users to perform interactive queries on live processes generating real-time information that can be rendered in multiple formats on multiple surfaces in the form of several desired visualizations simultaneously. To achieve this, we model various exploratory inspection and diagnostic tasks for deep learning training processes as specifications for streams using a map-reduce paradigm with which many data scientists are already familiar. Our design achieves generality and extensibility by defining composable primitives which is a fundamentally different approach than is used by currently available systems. The open source implementation of our system is available as TensorWatch project at https://github.com/microsoft/tensorwatch.Comment: Accepted at ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2019). Code available as TensorWatch project at https://github.com/microsoft/tensorwatc

Enabling semantic queries across federated bioinformatics databases

MOTIVATION: Data integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data available publicly. However, the heterogeneity of the different data sources, both at the syntactic and the semantic level, still poses significant challenges for achieving interoperability among biological databases. RESULTS: We introduce an ontology-based federated approach for data integration. We applied this approach to three heterogeneous data stores that span different areas of biological knowledge: (i) Bgee, a gene expression relational database; (ii) Orthologous Matrix (OMA), a Hierarchical Data Format 5 orthology DS; and (iii) UniProtKB, a Resource Description Framework (RDF) store containing protein sequence and functional information. To enable federated queries across these sources, we first defined a new semantic model for gene expression called GenEx. We then show how the relational data in Bgee can be expressed as a virtual RDF graph, instantiating GenEx, through dedicated relational-to-RDF mappings. By applying these mappings, Bgee data are now accessible through a public SPARQL endpoint. Similarly, the materialized RDF data of OMA, expressed in terms of the Orthology ontology, is made available in a public SPARQL endpoint. We identified and formally described intersection points (i.e. virtual links) among the three data sources. These allow performing joint queries across the data stores. Finally, we lay the groundwork to enable nontechnical users to benefit from the integrated data, by providing a natural language template-based search interface

Querying knowledge graphs in natural language.

Knowledge graphs are a powerful concept for querying large amounts of data. These knowledge graphs are typically enormous and are often not easily accessible to end-users because they require specialized knowledge in query languages such as SPARQL. Moreover, end-users need a deep understanding of the structure of the underlying data models often based on the Resource Description Framework (RDF). This drawback has led to the development of Question-Answering (QA) systems that enable end-users to express their information needs in natural language. While existing systems simplify user access, there is still room for improvement in the accuracy of these systems. In this paper we propose a new QA system for translating natural language questions into SPARQL queries. The key idea is to break up the translation process into 5 smaller, more manageable sub-tasks and use ensemble machine learning methods as well as Tree-LSTM-based neural network models to automatically learn and translate a natural language question into a SPARQL query. The performance of our proposed QA system is empirically evaluated using the two renowned benchmarks-the 7th Question Answering over Linked Data Challenge (QALD-7) and the Large-Scale Complex Question Answering Dataset (LC-QuAD). Experimental results show that our QA system outperforms the state-of-art systems by 15% on the QALD-7 dataset and by 48% on the LC-QuAD dataset, respectively. In addition, we make our source code available

Three-dimensional Models of Core-collapse Supernovae From Low-mass Progenitors With Implications for Crab

We present 3D full-sphere supernova simulations of non-rotating low-mass (~9 Msun) progenitors, covering the entire evolution from core collapse through bounce and shock revival, through shock breakout from the stellar surface, until fallback is completed several days later. We obtain low-energy explosions [~(0.5-1.0)x 10^{50} erg] of iron-core progenitors at the low-mass end of the core-collapse supernova (LMCCSN) domain and compare to a super-AGB (sAGB) progenitor with an oxygen-neon-magnesium core that collapses and explodes as electron-capture supernova (ECSN). The onset of the explosion in the LMCCSN models is modelled self-consistently using the Vertex-Prometheus code, whereas the ECSN explosion is modelled using parametric neutrino transport in the Prometheus-HOTB code, choosing different explosion energies in the range of previous self-consistent models. The sAGB and LMCCSN progenitors that share structural similarities have almost spherical explosions with little metal mixing into the hydrogen envelope. A LMCCSN with less 2nd dredge-up results in a highly asymmetric explosion. It shows efficient mixing and dramatic shock deceleration in the extended hydrogen envelope. Both properties allow fast nickel plumes to catch up with the shock, leading to extreme shock deformation and aspherical shock breakout. Fallback masses of <~5x10^{-3} Msun have no significant effects on the neutron star (NS) masses and kicks. The anisotropic fallback carries considerable angular momentum, however, and determines the spin of the newly-born NS. The LMCCSNe model with less 2nd dredge-up results in a hydrodynamic and neutrino-induced NS kick of >40 km/s and a NS spin period of ~30 ms, both not largely different from those of the Crab pulsar at birth.Comment: 47 pages, 27 figures, 6 tables; minor revisions, accepted by MNRA

A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL [version 1; peer review: 1 approved, 2 approved with reservations]

The increasing use of Semantic Web technologies in the life sciences, in particular the use of the Resource Description Framework (RDF) and the RDF query language SPARQL, opens the path for novel integrative analyses, combining information from multiple sources. However, analyzing evolutionary data in RDF is not trivial, due to the steep learning curve required to understand both the data models adopted by different RDF data sources, as well as the SPARQL query language. In this article, we provide a hands-on introduction to querying evolutionary data across multiple sources that publish orthology information in RDF, namely: The Orthologous MAtrix (OMA), the European Bioinformatics Institute (EBI) RDF platform, the Database of Orthologous Groups (OrthoDB) and the Microbial Genome Database (MBGD). We present four protocols in increasing order of complexity. In these protocols, we demonstrate through SPARQL queries how to retrieve pairwise orthologs, homologous groups, and hierarchical orthologous groups. Finally, we show how orthology information in different sources can be compared, through the use of federated SPARQL queries

Neutrino oscillations and the effect of the finite lifetime of the neutrino source

We consider a neutrino source at rest and discuss a condition for the existence of neutrino oscillations which derives from the finite lifetime $\tau_S$ of the neutrino source particle. This condition is present if the neutrino source is a free particle such that its wave function is non-stationary. For a Gaussian wave function and with some simplifying assumptions, we study the modification of the usual oscillation probability stemming from $\tau_S$. In the present accelerator experiments the effect of $\tau_S$ can be neglected. We discuss some experimental situations where the source lifetime becomes relevant in the oscillation formula.Comment: 13 pages latex file with 2 figure

Effects of neutrino oscillations and neutrino magnetic moments on elastic neutrino-electron scattering

We consider elastic antineutrino-electron scattering taking into account possible effects of neutrino masses and mixing and of neutrino magnetic moments and electric dipole moments. Having in mind antineutrinos produced in a nuclear reactor we compute, in particular, the weak-electromagnetic interference terms which are linear in the magnetic (electric dipole) moments and also in the neutrino masses. We show that these terms are, however, suppressed compared to the pure weak and electromagnetic cross section. We also comment upon the possibility of using the electromagnetic cross section to investigate neutrino oscillations.Comment: 12 pages, REVTEX file, no figures, submitted to Phys.Rev.

DIANA Scheduling Hierarchies for Optimizing Bulk Job Scheduling

The use of meta-schedulers for resource management in large-scale distributed systems often leads to a hierarchy of schedulers. In this paper, we discuss why existing meta-scheduling hierarchies are sometimes not sufficient for Grid systems due to their inability to re-organise jobs already scheduled locally. Such a job re-organisation is required to adapt to evolving loads which are common in heavily used Grid infrastructures. We propose a peer-to-peer scheduling model and evaluate it using case studies and mathematical modelling. We detail the DIANA (Data Intensive and Network Aware) scheduling algorithm and its queue management system for coping with the load distribution and for supporting bulk job scheduling. We demonstrate that such a system is beneficial for dynamic, distributed and self-organizing resource management and can assist in optimizing load or job distribution in complex Grid infrastructures.Comment: 8 pages, 9 figures. Presented at the 2nd IEEE Int Conference on eScience & Grid Computing. Amsterdam Netherlands, December 200

Activation of the Aryl Hydrocarbon Receptor Interferes with Early Embryonic Development.

The transcriptional program of early embryonic development is tightly regulated by a set of well-defined transcription factors that suppress premature expression of differentiation genes and sustain the pluripotent identity. It is generally accepted that this program can be perturbed by environmental factors such as chemical pollutants; however, the precise molecular mechanisms remain unknown. The aryl hydrocarbon receptor (AHR) is a widely expressed nuclear receptor that senses environmental stimuli and modulates target gene expression. Here, we have investigated the AHR interactome in embryonic stem cells by mass spectrometry and show that ectopic activation of AHR during early differentiation disrupts the differentiation program via the chromatin remodeling complex NuRD (nucleosome remodeling and deacetylation). The activated AHR/NuRD complex altered the expression of differentiation-specific genes that control the first two developmental decisions without affecting the pluripotency program. These findings identify a mechanism that allows environmental stimuli to disrupt embryonic development through AHR signaling