Scalable algorithms for scholarly figure mining and semantics

2016 ◽  
Author(s):  
Sagnik Ray Choudhury ◽  
Shuting Wang ◽  
C. Lee. Giles
Keyword(s):  
Scalable Algorithms

A distributional semantics-based information retrieval framework for online social networks

2021 ◽  
pp. 1-11
Author(s):  
V.S. Anoop ◽  
P. Deepak ◽  
S. Asharaf
Keyword(s):  
Social Networks ◽  
Information Retrieval ◽  
Online Social Networks ◽  
Latent Dirichlet Allocation ◽  
Relevant Information ◽  
Distributional Semantics ◽  
Scalable Algorithms ◽  
Mobile Platforms ◽  
Cancer Support ◽  
Efficient Extraction

Online social networks are considered to be one of the most disruptive platforms where people communicate with each other on any topic ranging from funny cat videos to cancer support. The widespread diffusion of mobile platforms such as smart-phones causes the number of messages shared in such platforms to grow heavily, thus more intelligent and scalable algorithms are needed for efficient extraction of useful information. This paper proposes a method for retrieving relevant information from social network messages using a distributional semantics-based framework powered by topic modeling. The proposed framework combines the Latent Dirichlet Allocation and distributional representation of phrases (Phrase2Vec) for effective information retrieval from online social networks. Extensive and systematic experiments on messages collected from Twitter (tweets) show this approach outperforms some state-of-the-art approaches in terms of precision and accuracy and better information retrieval is possible using the proposed method.

hIPPYlib

2021 ◽  
Vol 47 (2) ◽  
pp. 1-34
Author(s):  
Umberto Villa ◽  
Noemi Petra ◽  
Omar Ghattas
Keyword(s):  
Inverse Problems ◽  
Large Scale ◽  
Low Rank ◽  
Scalable Algorithms ◽  
Low Rank Approximation ◽  
Dimensional Parameter ◽  
Infinite Dimensional ◽  
Bayesian Inverse Problems ◽  
Rank Approximation ◽  
Extensible Software

We present an extensible software framework, hIPPYlib, for solution of large-scale deterministic and Bayesian inverse problems governed by partial differential equations (PDEs) with (possibly) infinite-dimensional parameter fields (which are high-dimensional after discretization). hIPPYlib overcomes the prohibitively expensive nature of Bayesian inversion for this class of problems by implementing state-of-the-art scalable algorithms for PDE-based inverse problems that exploit the structure of the underlying operators, notably the Hessian of the log-posterior. The key property of the algorithms implemented in hIPPYlib is that the solution of the inverse problem is computed at a cost, measured in linearized forward PDE solves, that is independent of the parameter dimension. The mean of the posterior is approximated by the MAP point, which is found by minimizing the negative log-posterior with an inexact matrix-free Newton-CG method. The posterior covariance is approximated by the inverse of the Hessian of the negative log posterior evaluated at the MAP point. The construction of the posterior covariance is made tractable by invoking a low-rank approximation of the Hessian of the log-likelihood. Scalable tools for sample generation are also discussed. hIPPYlib makes all of these advanced algorithms easily accessible to domain scientists and provides an environment that expedites the development of new algorithms.

scholarly journals Model-free, Model-based, and General Intelligence

2018 ◽  
Author(s):  
Hector Geffner
Keyword(s):  
Intelligent Systems ◽  
Human Mind ◽  
Writing Programs ◽  
Scalable Algorithms ◽  
Model Based ◽  
Model Free ◽  
Considerable Success ◽  
Black Boxes ◽  
Free Model ◽  
Intelligent Behavior

During the 60s and 70s, AI researchers explored intuitions about intelligence by writing programs that displayed intelligent behavior. Many good ideas came out from this work but programs written by hand were not robust or general. After the 80s, research increasingly shifted to the development of learners capable of inferring behavior and functions from experience and data, and solvers capable of tackling well-defined but intractable models like SAT, classical planning, Bayesian networks, and POMDPs. The learning approach has achieved considerable success but results in black boxes that do not have the flexibility, transparency, and generality of their model-based counterparts. Model-based approaches, on the other hand, require models and scalable algorithms. Model-free learners and model-based solvers have indeed close parallels with Systems 1 and 2 in current theories of the human mind: the first, a fast, opaque, and inflexible intuitive mind; the second, a slow, transparent, and flexible analytical mind. In this paper, I review developments in AI and draw on these theories to discuss the gap between model-free learners and model-based solvers, a gap that needs to be bridged in order to have intelligent systems that are robust and general.

scholarly journals Scalable Algorithms for NFA Multi-Striding and NFA-Based Deep Packet Inspection on GPUs

2016 ◽  
Vol 24 (3) ◽  
pp. 1704-1717 ◽  
Author(s):  
Matteo Avalle ◽  
Fulvio Risso ◽  
Riccardo Sisto
Keyword(s):  
Deep Packet Inspection ◽  
Scalable Algorithms ◽  
Packet Inspection

Scalable Algorithms for Multiple Network Alignment

2021 ◽  
pp. S592-S611
Author(s):  
Huda Nassar ◽  
Georgios Kollias ◽  
Ananth Grama ◽  
David F. Gleich
Keyword(s):  
Network Alignment ◽  
Scalable Algorithms ◽  
Multiple Network ◽  
Multiple Network Alignment

Scalable Algorithms for Server Allocation in Infostations

2010 ◽  
pp. 645-656
Author(s):  
Alan A. Bertossi ◽  
M. Cristina Pinotti ◽  
Phalguni Gupta
Keyword(s):  
Bin Packing ◽  
Multiprocessor Scheduling ◽  
Interval Graph ◽  
Scalable Algorithms ◽  
Coverage Area ◽  
Special Cases ◽  
Minimum Number ◽  
Server Allocation ◽  
On Line ◽  
Web Surfing

The server allocation problem arises in isolated infostations, where mobile users going through the coverage area require immediate high-bit rate communications such as web surfing, file transferring, voice messaging, email and fax. Given a set of service requests, each characterized by a temporal interval and a category, an integer k, and an integer hc for each category c, the problem consists in assigning a server to each request in such a way that at most k mutually simultaneous requests are assigned to the same server at the same time, out of which at most hc are of category c, and the minimum number of servers is used. Since this problem is computationally intractable, a scalable 2-approximation online algorithm is exhibited. Generalizations of the problem are considered, which contain bin-packing, multiprocessor scheduling, and interval graph coloring as special cases, and admit scalable on-line algorithms providing constant approximations.

Scalable Algorithms for Inverse and Uncertainty Modelling in Hydrology

2015 ◽  
pp. 467-486
Author(s):  
V. Vondrak ◽  
S. Kuchar ◽  
M. Golasowski ◽  
R. Vavrik ◽  
J. Martinovic ◽  
...  
Keyword(s):  
Scalable Algorithms ◽  
Uncertainty Modelling

scholarly journals GEOBIA at the Terapixel Scale: Toward Efficient Mapping of Small Woody Features from Heterogeneous VHR Scenes

2019 ◽  
Vol 8 (1) ◽  
pp. 46 ◽  
Author(s):  
François Merciol ◽  
Loïc Faucqueur ◽  
Bharath Damodaran ◽  
Pierre-Yves Rémy ◽  
Baudouin Desclée ◽  
...  
Keyword(s):  
High Resolution ◽  
Semantic Content ◽  
Scalable Algorithms ◽  
Large Area ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Wide Range ◽  
Geographic Object ◽  
Monitoring Service ◽  
Hierarchical Representations

Land cover mapping has benefited a lot from the introduction of the Geographic Object-Based Image Analysis (GEOBIA) paradigm, that allowed to move from a pixelwise analysis to a processing of elements with richer semantic content, namely objects or regions. However, this paradigm requires to define an appropriate scale, that can be challenging in a large-area study where a wide range of landscapes can be observed. We propose here to conduct the multiscale analysis based on hierarchical representations, from which features known as differential attribute profiles are derived over each single pixel. Efficient and scalable algorithms for construction and analysis of such representations, together with an optimized usage of the random forest classifier, provide us with a semi-supervised framework in which a user can drive mapping of elements such as Small Woody Features at a very large area. Indeed, the proposed open-source methodology has been successfully used to derive a part of the High Resolution Layers (HRL) product of the Copernicus Land Monitoring service, thus showing how the GEOBIA framework can be used in a big data scenario made of more than 38,000 Very High Resolution (VHR) satellite images representing more than 120 TB of data.

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