Physical Sciences and Mathematics Commons^™

Data For "Density Constrains Environmental Impacts Of Fluid Abstraction In Continental Lithium Brines", Daniel B. Corkran, David F. Boutt, Lee Ann Munk, Brendan J. Moran, Sarah Mcknight, Jordan Jenckes, Alexander Kirshen

Data and Datasets

This dataset contains all data used in the study "Density constrains environmental impacts of fluid abstraction in continental lithium brines." Data include all SEAWAT groundwater-flow model input and output files, which contain all data associated with the parametric modeling study. It also contains NDVI and total annual precipitation datasets used in the study's remote sensing analysis.

Selecting Robust Strategies When Players Do Not Know Exactly What Game They Are Playing, Oscar Samuel Veliz

Open Access Theses & Dissertations

Game theory is a tool for modeling multi-agent decision problems and has been used to great success in modeling and simulating problems such as poker, security, and trading agents. However, many real games are extremely large and complex with multiple agent interactions. One approach for solving these games is to use abstraction techniques to shrink the game to a form that can be solved by removing details and translating a solution back to the original.However, abstraction introduces error into the model. This research studies ways to analyze games, abstractions, and strategies that are robust to noise in the game.

Gaining …

The Activity Of Abstraction In Physical Chemistry Problem Solving And Instruction, Jessica M. Karch

Graduate Doctoral Dissertations

Productive problem solving, concept construction, and sense making occur through the core process of abstraction. Although the capacity for domain-general abstraction is developed at a young age, the role of abstraction in increasingly complex and disciplinary environments, such as those encountered in undergraduate STEM education, is not well understood. Undergraduate physical chemistry relies particularly heavily on abstraction because it uses many overlapping and imperfect mathematical models to represent and interpret phenomena occurring on multiple scales. To reconcile these models, extract meaning from them, and recognize when to apply them in problem solving requires processes of abstraction. This dissertation aims to …

Topic-Centric Unsupervised Multi-Document Summarization Of Scientific And News Articles, Amanuel Alambo, Cori Lohstroh, Erik Madaus, Swati Padhee, Brandy Foster, Tanvi Banerjee, Krishnaprasad Thirunarayan, Michael Raymer

Computer Science and Engineering Faculty Publications

Recent advances in natural language processing have enabled automation of a wide range of tasks, including machine translation, named entity recognition, and sentiment analysis. Automated summarization of documents, or groups of documents, however, has remained elusive, with many efforts limited to extraction of keywords, key phrases, or key sentences. Accurate abstractive summarization has yet to be achieved due to the inherent difficulty of the problem, and limited availability of training data. In this paper, we propose a topic-centric unsupervised multi-document summarization framework to generate extractive and abstractive summaries for groups of scientific articles across 20 Fields of Study (FoS) in …

Towards Interpreting Recurrent Neural Networks Through Probabilistic Abstraction, Guoliang Dong, Jingyi Wang, Jun Sun, Yang Zhang, Xinyu Wang, Ting Dai, Jin Song Dong, Xingen Wang

Research Collection School Of Computing and Information Systems

Neural networks are becoming a popular tool for solving many realworld problems such as object recognition and machine translation, thanks to its exceptional performance as an end-to-end solution. However, neural networks are complex black-box models, which hinders humans from interpreting and consequently trusting them in making critical decisions. Towards interpreting neural networks, several approaches have been proposed to extract simple deterministic models from neural networks. The results are not encouraging (e.g., low accuracy and limited scalability), fundamentally due to the limited expressiveness of such simple models.In this work, we propose an approach to extract probabilistic automata for interpreting an important …

N-Cycle Splines Over Sexy Rings, Jacob Tilden Cummings

Senior Projects Spring 2020

In this project we abstract the work of previous bard students by introducing the concept of splines over non-integers, non-euclidean domains, and even non-PIDs. We focus on n-cycles for some natural number n. We show that the concept of flow up class bases exist in PID splines the same way they do in integer splines, remarking the complications and intricacies that arise when abstracting from the integers to PIDs. We also start from scratch by finding a flow up class basis for n-cycle splines over the real numbers adjoin two indeterminates, denoted R[x,y] which necessitate more original techniques.

Representation And Reconstruction Of Linear, Time-Invariant Networks, Nathan Scott Woodbury

Theses and Dissertations

Network reconstruction is the process of recovering a unique structured representation of some dynamic system using input-output data and some additional knowledge about the structure of the system. Many network reconstruction algorithms have been proposed in recent years, most dealing with the reconstruction of strictly proper networks (i.e., networks that require delays in all dynamics between measured variables). However, no reconstruction technique presently exists capable of recovering both the structure and dynamics of networks where links are proper (delays in dynamics are not required) and not necessarily strictly proper.The ultimate objective of this dissertation is to develop algorithms capable of …

Learning Probabilistic Models For Model Checking: An Evolutionary Approach And An Empirical Study, Jingyi Wang, Jun Sun, Qixia Yuan, Jun Pang

Research Collection School Of Computing and Information Systems

Many automated system analysis techniques (e.g., model checking, model-based testing) rely on first obtaining a model of the system under analysis. System modeling is often done manually, which is often considered as a hindrance to adopt model-based system analysis and development techniques. To overcome this problem, researchers have proposed to automatically “learn” models based on sample system executions and shown that the learned models can be useful sometimes. There are however many questions to be answered. For instance, how much shall we generalize from the observed samples and how fast would learning converge? Or, would the analysis result based on …

Slides: Water Management In Spain, Teodoro Estrela

Coping with Water Scarcity in River Basins Worldwide: Lessons Learned from Shared Experiences (Martz Summer Conference, June 9-10)

Presenter: Teodoro Estrela, Júcar River Basin Authority, Spain

15 slides

Dynamic Redeployment To Counter Congestion Or Starvation In Vehicle Sharing Systems, Supriyo Ghosh, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Extensive usage of private vehicles has led to increased traffic congestion, carbon emissions, and usage of non-renewable resources. These concerns have led to the wide adoption of vehicle sharing (ex: bike sharing, car sharing) systems in many cities of the world. In vehicle-sharing systems, base stations (ex: docking stations for bikes) are strategically placed throughout a city and each of the base stations contain a pre-determined number of vehicles at the beginning of each day. Due to the stochastic and individualistic movement of customers,there is typically either congestion (more than required)or starvation (fewer than required) of vehicles at certain base …

The Effects Of Abstraction On Best Nblock First Search, Justin R. Redd

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Search is an important aspect of Artificial Intelligence. Efficiently searching for solutions to large problems is important. One way to scale search large in problems quickly is to divide the work between multiple processors. There are many ways to divide this work using abstractions. This thesis examines the previous way this has been done in the past and introduces other ways to more efficiently divide the work and search in parallel.

Computing Perception From Sensor Data, Payam Barnaghi, Frieder Ganz, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

This paper describes a framework for perception creation from sensor data. We propose using data abstraction techniques, in particular Symbolic Aggregate Approximation (SAX), to analyse and create patterns from sensor data. The created patterns are then linked to semantic descriptions that define thematic, spatial and temporal features, providing highly granular abstract representation of the raw sensor data. This helps to reduce the size of the data that needs to be communicated from the sensor nodes to the gateways or highlevel processing components. We then discuss a method that uses abstract patterns created by SAX method and occurrences of different observations …

Demonstration: Real-Time Semantic Analysis Of Sensor Streams, Harshal Patni, Cory Andrew Henson, Michael Cooney, Amit P. Sheth, Krishnaprasad Thirunarayan

Kno.e.sis Publications

The emergence of dynamic information sources – including sensor networks – has led to large streams of real-time data on the Web. Research studies suggest, these dynamic networks have created more data in the last three years than in the entire history of civilization, and this trend will only increase in the coming years [1]. With this coming data explosion, real-time analytics software must either adapt or die [2]. This paper focuses on the task of integrating and analyzing multiple heterogeneous streams of sensor data with the goal of creating meaningful abstractions, or features. These features are then temporally aggregated …

Demonstration: Secure - Semantics Empowered Rescue Environment, Pratikkumar Desai, Cory Andrew Henson, Pramod Anantharam, Amit P. Sheth

Kno.e.sis Publications

This paper demonstrates a Semantic Web enabled system for collecting and processing sensor data within a rescue environment. The real-time system collects heterogeneous raw sensor data from rescue robots through a wireless sensor network. The raw sensor data is converted to RDF using the Semantic Sensor Network (SSN) ontology and further processed to generate abstractions used for event detection in emergency scenarios.

Loss Of Vision: How Mathematics Turned Blind While It Learned To See More Clearly, Bernd Buldt, Dirk Schlimm

Bernd Buldt

To discuss the developments of mathematics that have to do with the introduction of new objects, we distinguish between ‘Aristotelian’ and ‘non-Aristotelian’ accounts of abstraction and mathematical ‘top-down’ and ‘bottom-up’ approaches. The development of mathematics from the 19th to the 20th century is then characterized as a move from a ‘bottom-up’ to a ‘top-down’ approach. Since the latter also leads to more abstract objects for which the Aristotelian account of abstraction is not well-suited, this development has also lead to a decrease of visualizations in mathematical practice.

Demonstration: Real-Time Semantic Analysis Of Sensor Streams, Harshal Kamlesh Patni, Cory Andrew Henson, Michael Cooney, Amit P. Sheth, Krishnaprasad Thirunarayan

Kno.e.sis Publications

The emergence of dynamic information sources - including sensor networks - has led to large streams of real-time data on the Web. Research studies suggest, these dynamic networks have created more data in the last three years than in the entire history of civilization, and this trend will only increase in the coming years. With this coming data explosion, real-time analytics software must either adapt or die. This paper focuses on the task of integrating and analyzing multiple heterogeneous streams of sensor data with the goal of creating meaningful abstractions, or features. These features are then temporally aggregated into feature …

Accelerating Reinforcement Learning Through The Discovery Of Useful Subgoals, Amy Mcgovern, Andrew G. Barto

Andrew G. Barto

An ability to adjust to changing environments and unforeseen circumstances is likely to be an important component of a successful autonomous space robot. This paper shows how to augment reinforcement learning algorithms with a method for automatically discovering certain types of subgoals online. By creating useful new subgoals while learning, the agent is able to accelerate learning on a current task and to transfer its expertise to related tasks through the reuse of its ability to attain subgoals. Subgoals are created based on commonalities across multiple paths to a solution. We cast the task of finding these commonalities as a …

Verifying Abstract Components Within Concrete Software Environments, Tonglaga Bao

Theses and Dissertations

In order to model check a software component which is not a standalone program, we need a model of the software which completes the program. This problem is important for software engineers who need to deploy an existing component into a new environment. The model is typically generated by abstracting the surrounding software environment in which the component will be executed. However, abstracting the surrounding software is a difficult and error-prone task, particularly when the surrounding software is a complex software artifact which can not be easily abstracted. In this dissertation, we present a new approach to the problem by …

Abstraction, Extension And Structural Auditing With The Umls Semantic Network, Yan Chen

Dissertations

The Unified Medical Language System (UMLS) is a two-level biomedical terminological knowledge base, consisting of the Metathesaurus (META) and the Semantic Network (SN), which is an upper-level ontology of broad categories called semantic types (STs). The two levels are related via assignments of one or more STs to each concept of the META.

Although the SN provides a high-level abstraction for the META, it is not compact enough. Various metaschemas, which are compact higher-level abstraction networks of the SN, have been derived. A methodology is presented to evaluate and compare two given metaschemas, based on their structural properties. A consolidation …

On-The-Fly Dynamic Dead Variable Analysis, Joel P. Self

Theses and Dissertations

State explosion in model checking continues to be the primary obstacle to widespread use of software model checking. The large input ranges of variables used in software is the main cause of state explosion. As software grows in size and complexity the problem only becomes worse. As such, model checking research into data abstraction as a way of mitigating state explosion has become more and more important. Data abstractions aim to reduce the effect of large input ranges. This work focuses on a static program analysis technique called dead variable analysis. The goal of dead variable analysis is to discover …

Magnifying-Lens Abstraction For Markov Decision Processes, Luca De Alfaro, Pritam Roy

Luca de Alfaro

We present a novel abstraction technique which allows the analysis of reachability and safety properties of Markov decision processes with very large state spaces. The technique, called magnifying-lens abstraction, copes with the state-explosion problem by partitioning the state-space into regions, and by computing upper and lower bounds for reachability and safety properties on the regions, rather than on the states. To compute these bounds, magnifying-lens abstraction iterates over the regions, considering the concrete states of each region in turn, as if one were sliding across the abstraction a magnifying lens which allowed viewing the concrete states. The algorithm adaptively refines …

Dynamic Dead Variable Analysis, Micah S. Lewis

Theses and Dissertations

Dynamic dead variable analysis (DDVA) extends traditional static dead variable analysis (SDVA) in the context of model checking through the use of run-time information. The analysis is run multiple times during the course of model checking to create a more precise set of dead variables. The DDVA is evaluated based on the amount of memory used to complete model checking relative to SDVA while considering the extra overhead required to implement DDVA. On several models with a complex control flow graph, DDVA reduces the amount of memory needed by 38-88MB compared to SDVA with a cost of 36 bytes of …

Modular Verification Of Timed Circuits Using Automatic Abstraction, Eric G. Mercer, Chris Myers, Hao Zheng

Faculty Publications

The major barrier that prevents the application of formal verification to large designs is state explosion. This paper presents a new approach for verification of timed circuits using automatic abstraction. This approach partitions the design into modules, each with constrained complexity. Before verification is applied to each individual module, irrelevant information to the behavior of the selected module is abstracted away. This approach converts a verification problem with big exponential complexity to a set of subproblems, each with small exponential complexity. Experimental results are promising in that they indicate that our approach has the potential of completing much faster while …

Accelerating Reinforcement Learning Through The Discovery Of Useful Subgoals, Amy Mcgovern, Andrew G. Barto

Computer Science Department Faculty Publication Series

An ability to adjust to changing environments and unforeseen circumstances is likely to be an important component of a successful autonomous space robot. This paper shows how to augment reinforcement learning algorithms with a method for automatically discovering certain types of subgoals online. By creating useful new subgoals while learning, the agent is able to accelerate learning on a current task and to transfer its expertise to related tasks through the reuse of its ability to attain subgoals. Subgoals are created based on commonalities across multiple paths to a solution. We cast the task of finding these commonalities as a …