15,113 research outputs found
Event selection for dynamical downscaling: a neural network approach for physically-constrained precipitation events
This study presents a new dynamical downscaling strategy for extreme events. It is based on a combination of statistical downscaling of coarsely resolved global model simulations and dynamical downscaling of specific extreme events constrained by the statistical downscaling part. The method is applied to precipitation extremes over the upper Aare catchment, an area in Switzerland which is characterized by complex terrain. The statistical downscaling part consists of an Artificial Neural Network (ANN) framework trained in a reference period. Thereby, dynamically downscaled precipitation over the target area serve as predictands and large-scale variables, received from the global model simulation, as predictors. Applying the ANN to long term global simulations produces a precipitation series that acts as a surrogate of the dynamically downscaled precipitation for a longer climate period, and therefore are used in the selection of events. These events are then dynamically downscaled with a regional climate model to 2 km. The results show that this strategy is suitable to constraint extreme precipitation events, although some limitations remain, e.g., the method has lower efficiency in identifying extreme events in summer and the sensitivity of extreme events to climate change is underestimated
Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning
Constrained multiagent reinforcement learning (C-MARL) is gaining importance
as MARL algorithms find new applications in real-world systems ranging from
energy systems to drone swarms. Most C-MARL algorithms use a primal-dual
approach to enforce constraints through a penalty function added to the reward.
In this paper, we study the structural effects of this penalty term on the MARL
problem. First, we show that the standard practice of using the constraint
function as the penalty leads to a weak notion of safety. However, by making
simple modifications to the penalty term, we can enforce meaningful
probabilistic (chance and conditional value at risk) constraints. Second, we
quantify the effect of the penalty term on the value function, uncovering an
improved value estimation procedure. We use these insights to propose a
constrained multiagent advantage actor critic (C-MAA2C) algorithm. Simulations
in a simple constrained multiagent environment affirm that our reinterpretation
of the primal-dual method in terms of probabilistic constraints is effective,
and that our proposed value estimate accelerates convergence to a safe joint
policy.Comment: 19 pages, 8 figures. Presented at L4DC 202
Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attacks
The commercial use of Machine Learning (ML) is spreading; at the same time,
ML models are becoming more complex and more expensive to train, which makes
Intellectual Property Protection (IPP) of trained models a pressing issue.
Unlike other domains that can build on a solid understanding of the threats,
attacks and defenses available to protect their IP, the ML-related research in
this regard is still very fragmented. This is also due to a missing unified
view as well as a common taxonomy of these aspects.
In this paper, we systematize our findings on IPP in ML, while focusing on
threats and attacks identified and defenses proposed at the time of writing. We
develop a comprehensive threat model for IP in ML, categorizing attacks and
defenses within a unified and consolidated taxonomy, thus bridging research
from both the ML and security communities
Phase Space Reconstruction from Accelerator Beam Measurements Using Neural Networks and Differentiable Simulations
Characterizing the phase space distribution of particle beams in accelerators
is a central part of accelerator understanding and performance optimization.
However, conventional reconstruction-based techniques either use simplifying
assumptions or require specialized diagnostics to infer high-dimensional (
2D) beam properties. In this Letter, we introduce a general-purpose algorithm
that combines neural networks with differentiable particle tracking to
efficiently reconstruct high-dimensional phase space distributions without
using specialized beam diagnostics or beam manipulations. We demonstrate that
our algorithm accurately reconstructs detailed 4D phase space distributions
with corresponding confidence intervals in both simulation and experiment using
a single focusing quadrupole and diagnostic screen. This technique allows for
the measurement of multiple correlated phase spaces simultaneously, which will
enable simplified 6D phase space distribution reconstructions in the future
Generalization with Lossy Affordances: Leveraging Broad Offline Data for Learning Visuomotor Tasks
The utilization of broad datasets has proven to be crucial for generalization
for a wide range of fields. However, how to effectively make use of diverse
multi-task data for novel downstream tasks still remains a grand challenge in
robotics. To tackle this challenge, we introduce a framework that acquires
goal-conditioned policies for unseen temporally extended tasks via offline
reinforcement learning on broad data, in combination with online fine-tuning
guided by subgoals in learned lossy representation space. When faced with a
novel task goal, the framework uses an affordance model to plan a sequence of
lossy representations as subgoals that decomposes the original task into easier
problems. Learned from the broad data, the lossy representation emphasizes
task-relevant information about states and goals while abstracting away
redundant contexts that hinder generalization. It thus enables subgoal planning
for unseen tasks, provides a compact input to the policy, and facilitates
reward shaping during fine-tuning. We show that our framework can be
pre-trained on large-scale datasets of robot experiences from prior work and
efficiently fine-tuned for novel tasks, entirely from visual inputs without any
manual reward engineering.Comment: CoRL 202
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
Inference of relative permeability curves in reservoir rocks with ensemble Kalman method
Multiphase flows through reservoir rocks are a universal and complex
phenomenon. Relative permeability is one of the primary determinants in
reservoir performance calculations. Accurate estimation of the relative
permeability is crucial for reservoir management and future production. In this
paper, we propose inferring relative permeability curves from sparse saturation
data with an ensemble Kalman method. We represent these curves through a series
of positive increments of relative permeability at specified saturation values,
which guarantees monotonicity within, and boundedness between, 0 and 1. The
proposed method is validated by the inference performances in two synthetic
benchmarks designed by SPE and a field-scale model developed by Equinor that
includes certain real-field features. The results indicate that the relative
permeability curves can be accurately estimated within the saturation intervals
having available observations and appropriately extrapolated to the remaining
saturations by virtue of the embedded constraints. The predicted well responses
are comparable to the ground truths, even though they are not included as the
observation. The study demonstrates the feasibility of using ensemble Kalman
method to infer relative permeability curves from saturation data, which can
aid in the predictions of multiphase flow and reservoir production
Multimodal spatio-temporal deep learning framework for 3D object detection in instrumented vehicles
This thesis presents the utilization of multiple modalities, such as image and lidar, to incorporate spatio-temporal information from sequence data into deep learning architectures for 3Dobject detection in instrumented vehicles. The race to autonomy in instrumented vehicles or self-driving cars has stimulated significant research in developing autonomous driver assistance systems (ADAS) technologies related explicitly to perception systems. Object detection plays a crucial role in perception systems by providing spatial information to its subsequent modules; hence, accurate detection is a significant task supporting autonomous driving. The advent of deep learning in computer vision applications and the availability of multiple sensing modalities such as 360° imaging, lidar, and radar have led to state-of-the-art 2D and 3Dobject detection architectures. Most current state-of-the-art 3D object detection frameworks consider single-frame reference. However, these methods do not utilize temporal information associated with the objects or scenes from the sequence data. Thus, the present research hypothesizes that multimodal temporal information can contribute to bridging the gap between 2D and 3D metric space by improving the accuracy of deep learning frameworks for 3D object estimations. The thesis presents understanding multimodal data representations and selecting hyper-parameters using public datasets such as KITTI and nuScenes with Frustum-ConvNet as a baseline architecture. Secondly, an attention mechanism was employed along with convolutional-LSTM to extract spatial-temporal information from sequence data to improve 3D estimations and to aid the architecture in focusing on salient lidar point cloud features. Finally, various fusion strategies are applied to fuse the modalities and temporal information into the architecture to assess its efficacy on performance and computational complexity. Overall, this thesis has established the importance and utility of multimodal systems for refined 3D object detection and proposed a complex pipeline incorporating spatial, temporal and attention mechanisms to improve specific, and general class accuracy demonstrated on key autonomous driving data sets
Network inference combining mutual information rate and statistical tests
In this paper, we present a method that combines information-theoretical and statistical approaches to infer connec- tivity in complex networks using time-series data. The method is based on estimations of the Mutual Information Rate for pairs of time-series and on statistical significance tests for connectivity acceptance using the false discovery rate method for multiple hypothesis testing. We provide the mathematical background on Mutual Information Rate, discuss the statistical significance tests and the false discovery rate. Further on, we present results for corre- lated normal-variates data, coupled circle and coupled logistic maps, coupled Lorenz systems and coupled stochastic Kuramoto phase oscillators. Following up, we study the effect of noise on the presented methodology in networks of coupled stochastic Kuramoto phase oscillators and of coupling heterogeneity degree on networks of coupled circle maps. We show that the method can infer the correct number and pairs of connected nodes, by means of receiver operating characteristic curves. In the more realistic case of stochastic data, we demonstrate its ability to infer the structure of the initial connectivity matrices. The method is also shown to recover the initial connectivity matrices for dynamics on the nodes of Erd ̋os-R ́enyi and small-world networks with varying coupling heterogeneity in their connections. The highlight of the proposed methodology is its ability to infer the underlying network connectivity based solely on the recorded datasets
- …