273 research outputs found
Logical Inference Techniques for Loop Parallelization
This paper presents a fully automatic approach to loop parallelization that integrates the use of static and run-time analysis and thus overcomes many known difficulties such as nonlinear and indirect array indexing and complex control flow. Our hybrid analysis framework validates the parallelization transformation by verifying the independence of the loop’s memory references. To this end it represents array references using the USR (uniform set representation) language and expresses the independence condition as an equation, S = ∅, where S is a set expression representing array indexes. Using a language instead of an array-abstraction representation for S results in a smaller number of conservative approximations but exhibits a potentially-high runtime cost. To alleviate this cost we introduce a language translation F from the USR set-expression language to an equally rich language of predicates (F(S) ⇒ S = ∅). Loop parallelization is then validated using a novel logic inference algorithm that factorizes the obtained complex predicates (F(S)) into a sequence of sufficient-independence conditions that are evaluated first statically and, when needed, dynamically, in increasing order of their estimated complexities. We evaluate our automated solution on 26 benchmarks from PERFECT-CLUB and SPEC suites and show that our approach is effective in parallelizing large, complex loops and obtains much better full program speedups than the Intel and IBM Fortran compilers
Active Learning for Optimal Intervention Design in Causal Models
Sequential experimental design to discover interventions that achieve a
desired outcome is a key problem in various domains including science,
engineering and public policy. When the space of possible interventions is
large, making an exhaustive search infeasible, experimental design strategies
are needed. In this context, encoding the causal relationships between the
variables, and thus the effect of interventions on the system, is critical for
identifying desirable interventions more efficiently. Here, we develop a causal
active learning strategy to identify interventions that are optimal, as
measured by the discrepancy between the post-interventional mean of the
distribution and a desired target mean. The approach employs a Bayesian update
for the causal model and prioritizes interventions using a carefully designed,
causally informed acquisition function. This acquisition function is evaluated
in closed form, allowing for fast optimization. The resulting algorithms are
theoretically grounded with information-theoretic bounds and provable
consistency results for linear causal models with known causal graph. We apply
our approach to both synthetic data and single-cell transcriptomic data from
Perturb-CITE-seq experiments to identify optimal perturbations that induce a
specific cell state transition. The causally informed acquisition function
generally outperforms existing criteria allowing for optimal intervention
design with fewer but carefully selected samples
Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation
The rapid progress in clinical data management systems and artificial
intelligence approaches enable the era of personalized medicine. Intensive care
units (ICUs) are the ideal clinical research environment for such development
because they collect many clinical data and are highly computerized
environments. We designed a retrospective clinical study on a prospective ICU
database using clinical natural language to help in the early diagnosis of
heart failure in critically ill children. The methodology consisted of
empirical experiments of a learning algorithm to learn the hidden
interpretation and presentation of the French clinical note data. This study
included 1386 patients' clinical notes with 5444 single lines of notes. There
were 1941 positive cases (36 % of total) and 3503 negative cases classified by
two independent physicians using a standardized approach. The multilayer
perceptron neural network outperforms other discriminative and generative
classifiers. Consequently, the proposed framework yields an overall
classification performance with 89 % accuracy, 88 % recall, and 89 % precision.
Furthermore, a generative autoencoder learning algorithm was proposed to
leverage the sparsity reduction that achieved 91% accuracy, 91% recall, and 91%
precision. This study successfully applied learning representation and machine
learning algorithms to detect heart failure from clinical natural language in a
single French institution. Further work is needed to use the same methodology
in other institutions and other languages.Comment: Submitting to IEEE Transactions on Biomedical Engineering. arXiv
admin note: text overlap with arXiv:2104.0393
Review of graph-based hazardous event detection methods for autonomous driving systems
Automated and autonomous vehicles are often required to operate in complex road environments with potential hazards that may lead to hazardous events causing injury or even death. Therefore, a reliable autonomous hazardous event detection system is a key enabler for highly autonomous vehicles (e.g., Level 4 and 5 autonomous vehicles) to operate without human supervision for significant periods of time. One promising solution to the problem is the use of graph-based methods that are powerful tools for relational reasoning. Using graphs to organise heterogeneous knowledge about the operational environment, link scene entities (e.g., road users, static objects, traffic rules) and describe how they affect each other. Due to a growing interest and opportunity presented by graph-based methods for autonomous hazardous event detection, this paper provides a comprehensive review of the state-of-the-art graph-based methods that we categorise as rule-based, probabilistic, and machine learning-driven. Additionally, we present an in-depth overview of the available datasets to facilitate hazardous event training and evaluation metrics to assess model performance. In doing so, we aim to provide a thorough overview and insight into the key research opportunities and open challenges
The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems using IoT, Big Data, and Machine Learning
The quality of air is closely linked with the life quality of humans,
plantations, and wildlife. It needs to be monitored and preserved continuously.
Transportations, industries, construction sites, generators, fireworks, and
waste burning have a major percentage in degrading the air quality. These
sources are required to be used in a safe and controlled manner. Using
traditional laboratory analysis or installing bulk and expensive models every
few miles is no longer efficient. Smart devices are needed for collecting and
analyzing air data. The quality of air depends on various factors, including
location, traffic, and time. Recent researches are using machine learning
algorithms, big data technologies, and the Internet of Things to propose a
stable and efficient model for the stated purpose. This review paper focuses on
studying and compiling recent research in this field and emphasizes the Data
sources, Monitoring, and Forecasting models. The main objective of this paper
is to provide the astuteness of the researches happening to improve the various
aspects of air polluting models. Further, it casts light on the various
research issues and challenges also.Comment: 30 pages, 11 figures, Wireless Personal Communications. Wireless Pers
Commun (2023
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A Framework for Analyzing Stochastic Optimization Algorithms Under Dependence
In this dissertation, a theoretical framework based on concentration inequalities for empirical processes is developed to better design iterative optimization algorithms and analyze their convergence properties in the presence of complex dependence between directions and step-sizes. Based on this framework, we proposed a stochastic away-step Frank-Wolfe algorithm and a stochastic pairwise-step Frank-Wolfe algorithm for solving strongly convex problems with polytope constraints and proved that both of those algorithms converge linearly to the optimal solution in expectation and almost surely. Numerical results showed that the proposed algorithms are faster and more stable than most of their competitors.
This framework can be applied for designing and analyzing stochastic algorithms with adaptive step-sizes that are based on local curvature for self-concordant optimization problems. Notably, we proposed and analyzed a stochastic BFGS algorithm without line-search, and proved that it converges linearly globally and super-linearly locally using the framework mentioned above. This is the first work that analyzes a fully stochastic BFGS algorithm, which also avoids time consuming or even impossible line-search steps.
A third class of problems that the empirical processes framework can be applied to is to study the optimization of compositions of stochastic functions. A multi-level Monte Carlo based unbiased gradient generation method is introduced into stochastic optimization algorithms for minimizing function compositions. Based on this, standard stochastic optimization algorithms can be applied to these problems directly
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