Subsurface simulations use computational models to predict the flow of fluids (e.g., oil, water, gas) through porous media. These simulations are pivotal in industrial applications such as petroleum production, where fast and accurate models are needed for high-stake decision making, for example, for well placement optimization and field development planning. Classical finite difference numerical simulators require massive computational resources to model large-scale real-world reservoirs. Alternatively, streamline simulators and data-driven surrogate models are computationally more efficient by relying on approximate physics models, however they are insufficient to model complex reservoir dynamics at scale.

Humans have the remarkable ability to recognize and acquire novel visual concepts in a zero-shot manner. Given a high-level, symbolic description of a novel concept in terms of previously learned visual concepts and their relations, humans can recognize novel concepts without seeing any examples. Moreover, they can acquire new concepts by parsing and communicating symbolic structures using learned visual concepts and relations. Endowing these capabilities in machines is pivotal in improving their generalization capability at inference time. In this work, we introduce Zero-shot Concept Recognition and Acquisition (ZeroC), a neuro-symbolic architecture that can recognize and acquire novel concepts in a zero-shot way. ZeroC represents concepts as graphs of constituent concept models (as nodes) and their relations (as edges). To allow inference time composition, we employ energy-based models (EBMs) to model concepts and relations. We design ZeroC architecture so that it allows a one-to-one mapping between a symbolic graph structure of a concept and its corresponding EBM, which for the first time, allows acquiring new concepts, communicating its graph structure, and applying it to classification and detection tasks (even across domains) at inference time. We introduce algorithms for learning and inference with ZeroC. We evaluate ZeroC on a challenging grid-world dataset which is designed to probe zero-shot concept recognition and acquisition, and demonstrate its capability.

Edge streams are commonly used to capture interactions in dynamic networks, such as email, social, or computer networks. The problem of detecting anomalies or rare events in edge streams has a wide range of applications. However, it presents many challenges due to lack of labels, a highly dynamic nature of interactions, and the entanglement of temporal and structural changes in the network. Current methods are limited in their ability to address the above challenges and to efficiently process a large number of interactions. Here, we propose F-FADE, a new approach for detection of anomalies in edge streams, which uses a novel frequency-factorization technique to efficiently model the time-evolving distributions of frequencies of interactions between node-pairs. The anomalies are then determined based on the likelihood of the observed frequency of each incoming interaction. F-FADE is able to handle in an online streaming setting a broad variety of anomalies with temporal and structural changes, while requiring only constant memory. Our experiments on one synthetic and six real-world dynamic networks show that F-FADE achieves state of the art performance and may detect anomalies that previous methods are unable to find.

Forecasting of geopolitical events is a notoriously difficult task, with experts failing to significantly outperform a random baseline across many types of forecasting events. One successful way to increase the performance of forecasting tasks is to turn to crowdsourcing: leveraging many forecasts from non-expert users. Simultaneously, advances in machine learning have led to models that can produce reasonable, although not perfect, forecasts for many tasks. Recent efforts have shown that forecasts can be further improved by ``hybridizing'' human forecasters: pairing them with the machine models in an effort to combine the unique advantages of both. In this demonstration, we present Synergistic Anticipation of Geopolitical Events (SAGE), a platform for human/computer interaction that facilitates human reasoning with machine models.

SnapVX is a high-performance solver for convex optimization problems defined on networks. For problems of this form, SnapVX provides a fast and scalable solution with guaranteed global convergence. It combines the capabilities of two open source software packages: Snap.py and CVXPY. Snap.py is a large scale graph processing library, and CVXPY provides a general modeling framework for small-scale subproblems. SnapVX offers a customizable yet easy-to-use Python interface with out-of- the- box functionality. Based on the Alternating Direction Method of Multipliers (ADMM), it is able to efficiently store, analyze, parallelize, and solve large optimization problems from a variety of different applications. Documentation, examples, and more can be found on the SnapVX website at snap.stanford.edu/snapvx.

Large networks are becoming a widely used abstraction for studying complex systems in a broad set of disciplines, ranging from social network analysis to molecular biology and neuroscience. Despite an increasing need to analyze and manipulate large networks, only a limited number of tools are available for this task. Here, we describe Stanford Network Analysis Platform (SNAP), a general-purpose, high-performance system that provides easy to use, high-level operations for analysis and manipulation of large networks. We present SNAP functionality, describe its implementational details, and give performance benchmarks. SNAP has been developed for single big-memory machines and it balances the trade-off between maximum performance, compact in-memory graph representation, and the ability to handle dynamic graphs where nodes and edges are being added or removed over time. SNAP can process massive networks with hundreds of millions of nodes and billions of edges. SNAP offers over 140 different graph algorithms that can efficiently manipulate large graphs, calculate structural properties, generate regular and random graphs, and handle attributes and meta-data on nodes and edges. Besides being able to handle large graphs, an additional strength of SNAP is that networks and their attributes are fully dynamic, they can be modified during the computation at low cost. SNAP is provided as an open source library in C++ as well as a module in Python. We also describe the Stanford Large Network Dataset, a set of social and information real-world networks and datasets, which we make publicly available. The collection is a complementary resource to our SNAP software and is widely used for development and benchmarking of graph analytics algorithms.

We present Ringo, a system for analysis of large graphs. Graphs provide a way to represent and analyze systems of interacting objects (people, proteins, webpages) with edges between the objects denoting interactions (friendships, physical interactions, links). Mining graphs provides valuable insights about individual objects as well as the relationships among them. In building Ringo, we take advantage of the fact that machines with large memory and many cores are widely available and also relatively affordable. This allows us to build an easy-to-use interactive high-performance graph analytics system. Graphs also need to be built from input data, which often resides in the form of relational tables. Thus, Ringo provides rich functionality for manipulating raw input data tables into various kinds of graphs. Furthermore, Ringo also provides over 200 graph analytics functions that can then be applied to constructed graphs. We show that a single big-memory machine provides a very attractive platform for performing analytics on all but the largest graphs as it offers excellent performance and ease of use as compared to alternative approaches. With Ringo, we also demonstrate how to integrate graph analytics with an iterative process of trial-and-error data exploration and rapid experimentation, common in data mining workloads.

The real-time information on news sites, blogs and social networking sites changes dynamically and spreads rapidly through the Web. Developing methods for handling such information at a massive scale requires that we think about how information content varies over time, how it is transmitted, and how it mutates as it spreads.

Article Optimal locking integrated with operational transformation in distributed real-time group editors Share on Authors: Chengzheng Sun School of Computing and Information Technology, Griffith University, Brisbane, Qld 4111, Australia School of Computing and Information Technology, Griffith University, Brisbane, Qld 4111, AustraliaView Profile , Rok Sosič School of Computing and Information Technology, Griffith University, Brisbane, Qld 4111, Australia School of Computing and Information Technology, Griffith University, Brisbane, Qld 4111, AustraliaView Profile Authors Info & Claims PODC '99: Proceedings of the eighteenth annual ACM symposium on Principles of distributed computingMay 1999 Pages 43–52https://doi.org/10.1145/301308.301322Online:01 May 1999Publication History 16citation497DownloadsMetricsTotal Citations16Total Downloads497Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

A significant amount of software development is evolutionary, involving the modification of already existing programs. To a large extent, the modified programs produce the same results as the original program. This similarity between the original program and the development program is utilized by relative debugging. Relative debugging is a new concept that enables the user to compare the execution of two programs by specifying the expected correspondences between their states. A relative debugger concurrently executes the programs, verifies the correspondences, and reports any differences found. We describe our novel debugger, called Guard, and its relative debugging capabilities. Guard is implemented by using our library of debugging routines, called Dynascope, which provides debugging primitives in heterogeneous networked environments. To demonstrate the capacity of Guard for debugging in heterogeneous environments, we describe an experiment in which the execution of two programs is compared across Internet. The programs are written in different programming languages and executing on different computing platforms. © 1997 by John Wiley & Sons, Ltd.

We present a Unison algorithm to evaluate arbitrarily complex Boolean expressions. This novel algorithm, based on the total differential of a Boolean function, enables fast evaluation of Boolean expressions in software. Any combination of Boolean operations can be packed into the bits of one computer word and evaluated in parallel by bitwise logical operations. Sample runs of the Unison algorithm show that many Boolean operations can evaluated in one clock cycle. The Unison algorithm is able to evaluate Boolean expressions at an execution speed that is comparable to compiled evaluation while retaining the flexibility of interpreted approaches. The algorithm lends itself well to many practical applications.

This paper discusses Nimrod, a tool for performing parametrised simulations over networks of loosely coupled workstations. Using Nimrod the user interactively generates a parametrised experiment. Nimrod then controls the distribution of jobs to machines and the collection of results. A simple graphical user interface which is built for each application allows the user to view the simulation in terms of their problem domain. The current version of Nimrod is implemented above OSF DCE and runs on DEC Alpha and IBM RS6000 workstations (including a 22 node SP2). Two different case studies are discussed as an illustration of the utility of the system.

Because large scientific codes are rarely static objects, developers are often faced with the tedious task of accounting for discrepancies between new and old versions. In this paper, we describe a new technique called relative debugging that addresses this problem by automating the process of comparing a modified code against a correct reference code. We examine the utility of the relative debugging technique by applying a relative debugger called Guard to a range of debugging problems in a large atmospheric circulation model. Our experience confirms the effectiveness of the approach. Using Guard, we are able to validate a new sequential version of the atmospheric model, and to identify the source of a significant discrepancy in a parallel version in a short period of time.

Abstract Debugging and performance measurement tools are becoming more important in the development and maintenance of increasingly complex software. These tools belong to a class of programs, called directors. Directors are programs that monitor and control other programs. Through monitoring, directors analyze program execution at runtime. Monitoring provides tracing primitives as well as access to program's variables, dynamic data structures, and its internal state. Results of the monitoring analysis can be used by the director itself to change the future program behavior or presented to a human user for an interactive review and possible feedback actions. The future program behavior is changed through controlling primitives which provide an organized way to modify data values as well as the program code. This paper presents a library of directing commands which enable the construction of powerful directors. The interface has been implemented in a Unix environment as a runtime subsystem running in the directed program's address space. The paper provides the description of the interface and the basic programming techniques in building directors. Examples of novel applications, illustrating the use of the directing interface, are demonstrated by the directors for the visualization of program control and structured snapshots.

Backtracking search is frequently applied to solve a constraint-based search problem, but it often suffers from exponential growth of computing time. We present an alternative to backtracking search: local search with conflict minimization. We have applied this general search framework to study a benchmark constraint-based search problem, the n-queens problem. An efficient local search algorithm for the n-queens problem was implemented. This algorithm, running in linear time, does not backtrack. It is capable of finding a solution for extremely large size n-queens problems. For example, on a workstation it can find a solution for 3000000 queens in less than 55 s.>

This paper introduces program directing, a new way of program interaction. Directing enables one program, the director, to monitor and to control another program, the executor. One important application of program directing is human interaction with complex computer simulations.

The n-queens problem is to place n queens on an n*n chessboard so that no two queens attack each other. The authors present two new algorithms, called queen search 2 (QS2) and queen search 3 (QS3). QS2 and QS3 are probabilistic local search algorithms, based on a gradient-based heuristic. These algorithms, running in almost linear time, are capable of finding a solution for extremely large n-queens problems. For example, QS3 can find a solution for 500000 queens in approximately 1.5 min.>