1,696 research outputs found
A graph theoretical perspective for the unsupervised clustering of free text corpora
This thesis introduces a robust end to end topic discovery framework that extracts a set of coherent topics stemming intrinsically from document similarities. Some topic clustering methods can support embedded vectors instead of traditional Bag-of-Words (BoW) representation. Some can be free from the number of topics hyperparameter and some others can extract a multi-scale relation between topics. However, no topic clustering method supports all these properties together. This thesis focuses on this gap in the literature by designing a framework that supports any type of document-level features especially the embedded vectors. This framework does not require any uninformed decision making about the underlying data such as the number of topics, instead, the framework extracts topics in multiple resolutions. To achieve this goal, we combine existing methods from natural language processing (NLP) for feature generation and graph theory, first for graph construction based on semantic document similarities, then for graph partitioning to extract corresponding topics in multiple resolutions. Finally, we use specific methods from statistical machine learning to obtain highly generalisable supervised models to deploy topic classifiers for the deployment of topic extraction in real-time. Our applications on both a noisy and specialised corpus of medical records (i.e., descriptions for patient incidents within the NHS) and public news articles in daily language show that our framework extracts coherent topics that have better quantitative benchmark scores than other methods in most cases. The resulting multi-scale topics in both applications enable us to capture specific details more easily and choose the relevant resolutions for the specific objective. This study contributes to topic clustering literature by introducing a novel graph theoretical perspective that provides a combination of new properties. These properties are multiple resolutions, independence from uninformed decisions about the corpus, and usage of recent NLP features, such as vector embeddings.Open Acces
FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search
We present FLASH (\textbf{F}ast \textbf{L}SH \textbf{A}lgorithm for
\textbf{S}imilarity search accelerated with \textbf{H}PC), a similarity search
system for ultra-high dimensional datasets on a single machine, that does not
require similarity computations and is tailored for high-performance computing
platforms. By leveraging a LSH style randomized indexing procedure and
combining it with several principled techniques, such as reservoir sampling,
recent advances in one-pass minwise hashing, and count based estimations, we
reduce the computational and parallelization costs of similarity search, while
retaining sound theoretical guarantees.
We evaluate FLASH on several real, high-dimensional datasets from different
domains, including text, malicious URL, click-through prediction, social
networks, etc. Our experiments shed new light on the difficulties associated
with datasets having several million dimensions. Current state-of-the-art
implementations either fail on the presented scale or are orders of magnitude
slower than FLASH. FLASH is capable of computing an approximate k-NN graph,
from scratch, over the full webspam dataset (1.3 billion nonzeros) in less than
10 seconds. Computing a full k-NN graph in less than 10 seconds on the webspam
dataset, using brute-force (), will require at least 20 teraflops. We
provide CPU and GPU implementations of FLASH for replicability of our results
Error Propagation after Reordering Attacks on Hierarchical State Estimation
Part 2: Infrastructure ProtectionInternational audienceState estimation is vital to the stability of control systems, especially in power systems, which rely heavily on measurement devices installed throughout wide-area power networks. Several researchers have analyzed the problems arising from bad data injection and topology errors, and have proposed protection and mitigation schemes. This chapter employs hierarchical state estimation based on the common weighted-least-squares formulation to study the propagation of faults in intermediate and top-level state estimates as a result of measurement reordering attacks on a single region in the bottom level. Although power grids are equipped with modern defense mechanisms such as those recommended by the ISO/IEC 62351 standard, reordering attacks are still possible. This chapter concentrates on how an inexpensive data swapping attack in one region in the lower level can influence the accuracy of other regions in the same level and upper levels, and force the system towards undesirable states. The results are validated using the IEEE 118-bus test case
Does poverty cause conflict? Isolating the causal origins of the conflict trap
Does poverty cause civil conflict? A considerable literature seeks to answer this question, yet concerns about reverse causality threaten the validity of extant conclusions. To estimate the impact of poverty on conflict and to determine whether the relationship between them is causal, it is necessary to identify a source of exogenous variation in poverty. We do this by introducing a robust instrument for poverty: a time-varying measure of international inequalities. We draw upon existing theories about the structural position of a country in the international economic network—specifically, the expectation that countries in the core tend to be wealthier and those on the periphery struggle to develop. This instrument is plausibly exogenous and satisfies the exclusion restriction, which suggests that it affects conflict only through its influence upon poverty. Instrumental variables probit regression is employed to demonstrate that the impact of poverty upon conflict appears to be causal
CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS
The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research
Reinforcement Learning: A Survey
This paper surveys the field of reinforcement learning from a
computer-science perspective. It is written to be accessible to researchers
familiar with machine learning. Both the historical basis of the field and a
broad selection of current work are summarized. Reinforcement learning is the
problem faced by an agent that learns behavior through trial-and-error
interactions with a dynamic environment. The work described here has a
resemblance to work in psychology, but differs considerably in the details and
in the use of the word ``reinforcement.'' The paper discusses central issues of
reinforcement learning, including trading off exploration and exploitation,
establishing the foundations of the field via Markov decision theory, learning
from delayed reinforcement, constructing empirical models to accelerate
learning, making use of generalization and hierarchy, and coping with hidden
state. It concludes with a survey of some implemented systems and an assessment
of the practical utility of current methods for reinforcement learning.Comment: See http://www.jair.org/ for any accompanying file
Bayesian stochastic blockmodeling
This chapter provides a self-contained introduction to the use of Bayesian
inference to extract large-scale modular structures from network data, based on
the stochastic blockmodel (SBM), as well as its degree-corrected and
overlapping generalizations. We focus on nonparametric formulations that allow
their inference in a manner that prevents overfitting, and enables model
selection. We discuss aspects of the choice of priors, in particular how to
avoid underfitting via increased Bayesian hierarchies, and we contrast the task
of sampling network partitions from the posterior distribution with finding the
single point estimate that maximizes it, while describing efficient algorithms
to perform either one. We also show how inferring the SBM can be used to
predict missing and spurious links, and shed light on the fundamental
limitations of the detectability of modular structures in networks.Comment: 44 pages, 16 figures. Code is freely available as part of graph-tool
at https://graph-tool.skewed.de . See also the HOWTO at
https://graph-tool.skewed.de/static/doc/demos/inference/inference.htm
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