502 research outputs found
Using Wave-Packet Interferometry to Monitor the External Vibrational Control of Electronic Excitation Transfer
We investigate the control of electronic energy transfer in molecular dimers
through the preparation of specific vibrational coherences prior to electronic
excitation, and its observation by nonlinear wave-packet interferometry.
Laser-driven coherent nuclear motion can affect the instantaneous resonance
between site-excited electronic states and thereby influence short-time
electronic excitation transfer (EET). We first illustrate this control
mechanism with calculations on a dimer whose constituent monomers undergo
harmonic vibrations. We then consider the use of nonlinear wave-packet
interferometry (nl-WPI) experiments to monitor the nuclear dynamics
accompanying EET in general dimer complexes following impulsive vibrational
excitation by a sub-resonant control pulse (or control pulse sequence). In
measurements of this kind, two pairs of polarized phase-related femtosecond
pulses following the control pulse generate superpositions of coherent nuclear
wave packets in optically accessible electronic states. Interference
contributions to the time- and frequency-integrated fluorescence signal due to
overlaps among the superposed wave packets provide amplitude-level information
on the nuclear and electronic dynamics. We derive the basic expression for a
control-pulse-dependent nl-WPI signal. The electronic transition moments of the
constituent monomers are assumed to have a fixed relative orientation, while
the overall orientation of the complex is distributed isotropically. We include
the limiting case of coincident arrival by pulses within each phase-related
pair in which control-influenced nl-WPI reduces to a fluorescence-detected
pump-probe difference experiment. Numerical calculations of pump-probe signals
based on these theoretical expressions are presented in the following paper
A black-Box adversarial attack for poisoning clustering
Clustering algorithms play a fundamental role as tools in decision-making and sensible automation pro-cesses. Due to the widespread use of these applications, a robustness analysis of this family of algorithms against adversarial noise has become imperative. To the best of our knowledge, however, only a few works have currently addressed this problem. In an attempt to fill this gap, in this work, we propose a black-box adversarial attack for crafting adversarial samples to test the robustness of clustering algo-rithms. We formulate the problem as a constrained minimization program, general in its structure and customizable by the attacker according to her capability constraints. We do not assume any information about the internal structure of the victim clustering algorithm, and we allow the attacker to query it as a service only. In the absence of any derivative information, we perform the optimization with a custom approach inspired by the Abstract Genetic Algorithm (AGA). In the experimental part, we demonstrate the sensibility of different single and ensemble clustering algorithms against our crafted adversarial samples on different scenarios. Furthermore, we perform a comparison of our algorithm with a state-of-the-art approach showing that we are able to reach or even outperform its performance. Finally, to highlight the general nature of the generated noise, we show that our attacks are transferable even against supervised algorithms such as SVMs, random forests and neural networks. (c) 2021 Elsevier Ltd. All rights reserved
3D GLACIER MAPPING BY MEANS OF SATELLITE STEREO IMAGES: THE BELVEDERE GLACIER CASE STUDY IN THE ITALIAN ALPS
The authors group is within the Glacier Lab of Politecnico di Torino (part of the CC-LAB, a laboratory for climate change monitoring), which is working on glacier monitoring since 2016, mainly exploiting Geomatics techniques to measure the extent and to model the surface of glaciers over the years. Measurement campaigns were carried out within the ASP (Alta Scuola Politecnica – Poliecnico di Torino e Milano) DREAM projects (Drone tEchnnology for wAter resources and hydrologic hazard Monitoring) The manuscript is focused on a specific case study related to the Belvedere glacier, a valley glacier located in northern Italy.In the framework of the Belvedere glacier monitoring, several Geomatics approaches have already been applied in the last four years by the cc-glacier-lab and DREAM Projects with the goal to monitor both the extent of the glacier and its surface. Such monitoring enables the multi-temporal comparison of the glacier digital surface model (DSM), highlighting areas of ice loss and gain. Considering the limitations of aerial surveys in high altitude environments, the authors started assessing the suitability of a satellite based approach, mainly focusing on positional accuracy assessment. The paper is focused on a monitoring based on a high resolution (0.5 m) satellite optical stereo pair. Several tests were carried out with the goal to test the 3D positional accuracies, assessing the impact of different configurations of Ground Control Point (GCP) in terms of numerosity and distribution and focusing on the DSM validation. The results demonstrated the fit-for-purpose of a satellite-based approach for glacier monitoring
Foreground-Background Segmentation Based on Codebook and Edge Detector
Background modeling techniques are used for moving object detection in video.
Many algorithms exist in the field of object detection with different purposes.
In this paper, we propose an improvement of moving object detection based on
codebook segmentation. We associate the original codebook algorithm with an
edge detection algorithm. Our goal is to prove the efficiency of using an edge
detection algorithm with a background modeling algorithm. Throughout our study,
we compared the quality of the moving object detection when codebook
segmentation algorithm is associated with some standard edge detectors. In each
case, we use frame-based metrics for the evaluation of the detection. The
different results are presented and analyzed.Comment: to appear in the 10th International Conference on Signal Image
Technology & Internet Based Systems, 201
Deep learning-based approach for tomato classification in complex scenes
Tracking ripening tomatoes is time consuming and labor intensive. Artificial
intelligence technologies combined with those of computer vision can help users
optimize the process of monitoring the ripening status of plants. To this end,
we have proposed a tomato ripening monitoring approach based on deep learning
in complex scenes. The objective is to detect mature tomatoes and harvest them
in a timely manner. The proposed approach is declined in two parts. Firstly,
the images of the scene are transmitted to the pre-processing layer. This
process allows the detection of areas of interest (area of the image containing
tomatoes). Then, these images are used as input to the maturity detection
layer. This layer, based on a deep neural network learning algorithm,
classifies the tomato thumbnails provided to it in one of the following five
categories: green, brittle, pink, pale red, mature red. The experiments are
based on images collected from the internet gathered through searches using
tomato state across diverse languages including English, German, French, and
Spanish. The experimental results of the maturity detection layer on a dataset
composed of images of tomatoes taken under the extreme conditions, gave a good
classification rate
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