17,597 research outputs found
Aerial images and machine learning methods to emulate the late blight severity in potato crops
Assessment of Phytophthora infestans’ incidence and severity are frequently performed
based on visual crop inspection, which is a labor-intensive task prone to errors associated with
its subjectivity. Therefore, alternative methods to relate disease incidence and severity with
changes in crop traits are of great interest. Optical imagery in the visible and near-infrared (VisNIR) can detect changes in crop traits caused by pathogen development. In addition, Unmanned
Aerial Vehicles (UAV) with cameras on board have flexible data collection capabilities allowing
adjustments considering the trade-off between data throughput and its resolution.
This work presents a quantitative prediction of the severity of the disease caused by
Phytophthora infestans in potato crops using image processing and machine learning (ML)
algorithms such as Random Forests (RF) and Extreme Gradient Boost (XGBoost). The ML
algorithms were trained using datasets from multispectral data captured at the canopy level
with a UAV carrying a multispectral camera. The results indicate that RF and XGBoost using 11
classes with 18 bands, including vegetation indexes and band features, can predict late blight
severity on potato crops with an acceptable accuracy of 81.02% for RF and 74.19% for RF
XGBoost
Dynamic Feature Engineering and model selection methods for temporal tabular datasets with regime changes
The application of deep learning algorithms to temporal panel datasets is
difficult due to heavy non-stationarities which can lead to over-fitted models
that under-perform under regime changes. In this work we propose a new machine
learning pipeline for ranking predictions on temporal panel datasets which is
robust under regime changes of data. Different machine-learning models,
including Gradient Boosting Decision Trees (GBDTs) and Neural Networks with and
without simple feature engineering are evaluated in the pipeline with different
settings. We find that GBDT models with dropout display high performance,
robustness and generalisability with relatively low complexity and reduced
computational cost. We then show that online learning techniques can be used in
post-prediction processing to enhance the results. In particular, dynamic
feature neutralisation, an efficient procedure that requires no retraining of
models and can be applied post-prediction to any machine learning model,
improves robustness by reducing drawdown in regime changes. Furthermore, we
demonstrate that the creation of model ensembles through dynamic model
selection based on recent model performance leads to improved performance over
baseline by improving the Sharpe and Calmar ratios of out-of-sample prediction
performances. We also evaluate the robustness of our pipeline across different
data splits and random seeds with good reproducibility of results
Large deviations for hyperbolic -nearest neighbor balls
We prove a large deviation principle for the point process of large Poisson
-nearest neighbor balls in hyperbolic space. More precisely, we consider a
stationary Poisson point process of unit intensity in a growing sampling window
in hyperbolic space. We further take a growing sequence of thresholds such that
there is a diverging expected number of Poisson points whose -nearest
neighbor ball has a volume exceeding this threshold. Then, the point process of
exceedances satisfies a large deviation principle whose rate function is
described in terms of a relative entropy. The proof relies on a fine
coarse-graining technique such that inside the resulting blocks the exceedances
are approximated by independent Poisson point processes.Comment: 18 page
Analysis and Design of Detection for Liver Cancer using Particle Swarm Optimization and Decision Tree
Liver cancer is taken as a major cause of death all over the world. According to WHO (World Health Organization) every year 9.6 million peoples are died due to cancer worldwide. It is one of the eighth most leading causes of death in women and fifth in men as reported by the American Cancer Society. The number of death rate due to cancer is projected to increase by45 percent in between 2008 to 2030. The most common cancers are lung, breast, and liver, colorectal. Approximately 7, 82,000 peoples are died due to liver cancer each year. The most efficient way to decrease the death rate cause of liver cancer is to treat the diseases in the initial stage. Early treatment depends upon the early diagnosis, which depends on reliable diagnosis methods. CT imaging is one of the most common and important technique and it acts as an imaging tool for evaluating the patients with intuition of liver cancer. The diagnosis of liver cancer has historically been made manually by a skilled radiologist, who relied on their expertise and personal judgement to reach a conclusion. The main objective of this paper is to develop the automatic methods based on machine learning approach for accurate detection of liver cancer in order to help radiologists in the clinical practice. The paper primary contribution to the process of liver cancer lesion classification and automatic detection for clinical diagnosis. For the purpose of detecting liver cancer lesions, the best approaches based on PSO and DPSO have been given. With the help of the C4.5 decision tree classifier, wavelet-based statistical and morphological features were retrieved and categorised
Endperiodic maps via pseudo-Anosov flows
We show that every atoroidal endperiodic map of an infinite-type surface can
be obtained from a depth one foliation in a fibered hyperbolic 3-manifold,
reversing a well-known construction of Thurston. This can be done
almost-transversely to the canonical suspension flow, and as a consequence we
recover the Handel-Miller laminations of such a map directly from the fibered
structure. We also generalize from the finite-genus case the relation between
topological entropy, growth rates of periodic points, and growth rates of
intersection numbers of curves. Fixing the manifold and varying the depth one
foliations, we obtain a description of the Cantwell-Conlon foliation cones and
a proof that the entropy function on these cones is continuous and convex.Comment: 50 pages, 12 figure
Cavity-Catalyzed Hydrogen Transfer Dynamics in an Entangled Molecular Ensemble under Vibrational Strong Coupling
Microcavities have been shown to influence the reactivity of molecular
ensembles by strong coupling of molecular vibrations to quantized cavity modes.
In quantum mechanical treatments of such scenarios, frequently idealized models
with single molecules and scaled, effective molecule-cavity interactions or
alternatively ensemble models with simplified model Hamiltonians are used. In
this work, we go beyond these models by applying an ensemble variant of the
Pauli-Fierz Hamiltonian for vibro-polaritonic chemistry and numerically solve
the underlying time-dependent Schr\"odinger equation to study the
cavity-induced quantum dynamics in an ensemble of thioacetylacetone (TAA)
molecules undergoing hydrogen transfer under vibrational strong coupling (VSC)
conditions. Beginning with a single molecule coupled to a single cavity mode,
we show that the cavity indeed enforces hydrogen transfer from an enol to an
enethiol configuration with transfer rates significantly increasing with
light-matter interaction strength. This positive effect of the cavity on
reaction rates is different from several other systems studied so far, where a
retarding effect of the cavity on rates was found. It is argued that the cavity
``catalyzes'' the reaction by transfer of virtual photons to the molecule. The
same concept applies to ensembles with up to TAA molecules coupled to a
single cavity mode, where an additional, significant, ensemble-induced
collective isomerization rate enhancement is found. The latter is traced back
to complex entanglement dynamics of the ensemble, which we quantify by means of
von Neumann-entropies. A non-trivial dependence of the dynamics on ensemble
size is found, clearly beyond scaled single-molecule models, which we interpret
as transition from a multi-mode Rabi to a system-bath-type regime as
increases.Comment: Manuscript 9 pages, 5 figures (minor changes in v2). Supplementary
Information 7 pages, 5 figures (Section III rewritten in v2 after
peer-review
Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthroughs
in a variety of areas, including computer vision, natural language
understanding, speech recognition, and reinforcement learning. Specialized,
high-performing neural architectures are crucial to the success of deep
learning in these areas. Neural architecture search (NAS), the process of
automating the design of neural architectures for a given task, is an
inevitable next step in automating machine learning and has already outpaced
the best human-designed architectures on many tasks. In the past few years,
research in NAS has been progressing rapidly, with over 1000 papers released
since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized
and comprehensive guide to neural architecture search. We give a taxonomy of
search spaces, algorithms, and speedup techniques, and we discuss resources
such as benchmarks, best practices, other surveys, and open-source libraries
Countermeasures for the majority attack in blockchain distributed systems
La tecnología Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus características únicas que la hacen ideal para registrar, verificar y administrar información de diferentes transacciones. A pesar de esto, Blockchain se enfrenta a diferentes problemas de seguridad, siendo el ataque del 51% o ataque mayoritario uno de los más importantes. Este consiste en que uno o más mineros tomen el control de al menos el 51% del Hash extraído o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnología. Este trabajo se enfocó en diseñar e implementar estrategias de detección y mitigación de ataques mayoritarios (51% de ataque) en un sistema distribuido Blockchain, a partir de la caracterización del comportamiento de los mineros. Para lograr esto, se analizó y evaluó el Hash Rate / Share de los mineros de Bitcoin y Crypto Ethereum, seguido del diseño e implementación de un protocolo de consenso para controlar el poder de cómputo de los mineros. Posteriormente, se realizó la exploración y evaluación de modelos de Machine Learning para detectar software malicioso de tipo Cryptojacking.DoctoradoDoctor en Ingeniería de Sistemas y Computació
Curie-law crossover in spin liquids
The Curie-Weiss law is widely used to estimate the strength of frustration in
frustrated magnets. However, the Curie-Weiss law was originally derived as an
estimate of magnetic correlations close to a mean-field phase transition, which
-- by definition -- is absent in spin liquids. Instead, the susceptibility of
spin liquids is known to undergo a Curie-law crossover between two magnetically
disordered regimes. Here, we study the generic aspect of the Curie-law
crossover by comparing a variety of frustrated spin models in two and three
dimensions, using both classical Monte Carlo simulations and analytical Husimi
tree calculations. Husimi tree calculations fit remarkably well the simulations
for all temperatures and almost all lattices. We also propose a Husimi Ansatz
for the reduced susceptibility , to be used in complement to the
traditional Curie-Weiss fit in order to estimate the Curie-Weiss temperature
. Applications to materials are discussed.Comment: 26 pages, 15 figure
Deep learning health management diagnostics applied to the NIST smoke experiments
Fire is one of the most important hazards that must be considered in advanced nuclear power plant safety assessments. The Nuclear Regulatory Commission (NRC) has developed a large collection of experimental data and associated analyses related to the study of fire safety. In fact, computational fire models are based on quantitative comparisons to those experimental data. During the modeling process, it is important to develop diagnostic health management systems to check the equipment status in fire processes. For example, a fire sensor does not directly provide accurate and complex information that nuclear power plants (NPPs) require. With the assistance of the machine learning method, NPP operators can directly get information on local, ignition, fire material of an NPP fire, instead of temperature, smoke obscuration, gas concentration, and alarm signals. In order to improve the predictive capabilities, this work demonstrates how the deep learning classification method can be used as a diagnostic tool in a specific set of fire experiments. Through a single input from a sensor, the deep learning tool can predict the location and type of fire. This tool also has the capability to provide automatic signals to potential passive fire safety systems. In this work, test data are taken from a specific set of the National Institute of Standards and Technology (NIST) fire experiments in a residential home and analyzed by using the machine learning classification models. The networks chosen for comparison and evaluation are the dense neural networks, convolutional neural networks, long short-term memory networks, and decision trees. The dense neural network and long short-term memory network produce similar levels of accuracy, but the convolutional neural network produces the highest accuracy
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