Bank of America Quartz WorldMap Infrastructure Monitoring

Bank of America’s Quartz platform, which is used for developing all internal applications by the company, is made up of over 600 application and database servers deployed in over 25 data centers globally. There are many variables that can contribute to issues in the environment for example CPU/memory consumption, disk space, network latency, replication latency, etc. Our team was responsible for building a web based interactive world map, named “Quartz WorldMap Infrastructure Monitoring”, which could graphically show the environment and alert when key performance thresholds are breached

Training Heterogeneous Features in Sequence to Sequence Tasks: Latent Enhanced Multi-filter Seq2Seq Model

In language processing, training data with extremely large variance may lead to difficulty in the language model's convergence. It is difficult for the network parameters to adapt sentences with largely varied semantics or grammatical structures. To resolve this problem, we introduce a model that concentrates the each of the heterogeneous features in the input sentences. Building upon the encoder-decoder architecture, we design a latent-enhanced multi-filter seq2seq model (LEMS) that analyzes the input representations by introducing a latent space transformation and clustering. The representations are extracted from the final hidden state of the encoder and lie in the latent space. A latent space transformation is applied for enhancing the quality of the representations. Thus the clustering algorithm can easily separate samples based on the features of these representations. Multiple filters are trained by the features from their corresponding clusters, and the heterogeneity of the training data can be resolved accordingly. We conduct two sets of comparative experiments on semantic parsing and machine translation, using the Geo-query dataset and Multi30k English-French to demonstrate the enhancement our model has made respectively.Comment: Accepted to Intelligent Systems Conference 202

Mathematical Modeling of Influenza Viruses

We first start with the simplest SIR model to describe the transmission of communicable disease through individuals. We analyze the SIR model and the SEIR model with periodic transmission rates. Then we focus on the two-strain SIR model with constant transmission rate. The two-strain model displays three basic relationships between the two viruses. These relationships are determined by the existence and stability of each equilibrium point. If there is no stable equilibrium point, the two-strain model has periodic solutions. We also study the two-strain SIR model with periodic transmission rate. The last part of this project conerns patterns observed from data downloaded from the World Health Organization (WHO) on the infective individuals of H1N1(77), H1N1(09), and H3N2(68) viruses

Crowdsourcing: Today and Tomorrow

This project focuses on crowdsourcing, the practice of outsourcing activities that are traditionally performed by a small group of professionals to an unknown, large community of individuals. Our study examines how crowdsourcing has become an important form of labor organization, what major forms of crowdsourcing exist currently, and which trends of crowdsourcing will have potential impacts on the society in the future. The study is conducted through literature study on the derivation and development of crowdsourcing, through examination on current major crowdsourcing platforms, and through surveys and interviews with crowdsourcing participants on their experiences and motivations

Map Based Discovery of Hydrologic Data in the HydroShare Collaboration Environment

Data discovery refers to the process of locating pre-existing data for use in new research. In the HydroShare collaboration environment for water science, there are more than twenty kinds of data that can be discovered, including data from specific sites on the globe, data corresponding to regions on the globe, and data with no geospatial meaning, such as laboratory experiment results. This paper discusses lessons learned in building a data discovery system for HydroShare. This was a surprisingly difficult problem; default behaviors of software components were unacceptable, use cases suggested conflicting approaches, and crafting a geographic view of a large number of candidate resources was subject to the limits imposed by web browsers, existing software capabilities, human perception, and software performance. The resulting software was a complex melding of user needs, software capabilities, and performance requirements

Grafting Modification of the Reactive Core-Shell Particles to Enhance the Toughening Ability of Polylactide

In order to overcome the brittleness of polylactide (PLA), reactive core-shell particles (RCS) with polybutadiene as core and methyl methacrylate-co-styrene-co-glycidyl methacrylate as shell were prepared to toughen PLA. Tert-dodecyl mercaptan (TDDM) was used as chain transfer agent to modify the grafting properties (such as grafting degree, shell thickness, internal and external grafting) of the core-shell particles. The introduction of TDDM decreased the grafting degree, shell thickness and the Tg of the core phase. When the content of TDDM was lower than 1.15%, the RCS particles dispersed in the PLA matrix uniformly—otherwise, agglomeration took place. The addition of RCS particles induced a higher cold crystallization temperature and a lower melting temperature of PLA which indicated the decreased crystallization ability of PLA. Dynamic mechanical analysis (DMA) results proved the good miscibility between PLA and the RCS particles and the increase of TDDM in RCS induced higher storage modulus of PLA/RCS blends. Suitable TDDM addition improved the toughening ability of RCS particles for PLA. In the present research, PLA/RCS-T4 (RCS-T4: the reactive core-shell particles with 0.76 wt % TDDM addition) blends displayed much better impact strength than other blends due to the easier cavitation/debonding ability and good dispersion morphology of the RCS-T4 particles. When the RCS-T4 content was 25 wt %, the impact strength of PLA/RCS-T4 blend reached 768 J/m, which was more than 25 times that of the pure PLA

HydroShare: Advancing Hydrology through Collaborative Data and Model Sharing

HydroShare is an online, collaborative system for open sharing of hydrologic data, analytical tools, and models.  It supports the sharing of and collaboration around “resources” which are defined by standardized content types for data formats and models commonly used in hydrology.  These include time series, geographic grids and shapes, multidimensional space-time data as well as models and model instances. This poster illustrates the HydroShare collaborative environment and web based services developed to support the sharing and processing of hydrologic data and models.  With HydroShare you can: Share your data and models with colleagues; Manage who has access to the content that you share; Share, access, visualize and manipulate a broad set of hydrologic data types and models; Use the web services application programming interface (API) to program automated and client access; Publish data and models and obtain a citable digital object identifier (DOI); Aggregate your resources into collections; Discover and access data and models published by others; Use web apps to visualize, analyze and run models on data in HydroShare.  The capability to assign DOIs to HydroShare resources means that they are permanently citable helping researchers who share their data get credit for the data published.  Models, and Model Instances, which in HydroShare are a model application to a specific site with its input and output data can also receive DOI's.  Collections allow multiple resources from a study to be aggregated together providing a comprehensive archival record of the research outcomes, supporting transparency and reproducibility, thereby enhancing trust in the findings.  Reuse to support additional research is also enabled.  HydroShare supports web apps to act on resources for cloud (server) based visualization and analysis, including large scale geographic and digital elevation model analysis at the CyberGIS center at the National Center for Supercomputing Applications (NCSA) and execution of SWAT and RHESSys models.<br