Title: Teaching Numerical Methods with iPython
Abstract: We discuss the results of a first attempt to introduce iPython as a tool to teach numerics to freshmen of the Bachelor Degree in Computer Science at Supsi. After a general introduction about the motivations and the possible advantages of that choice, a number of demonstrative examples related to different numerical algorithms (e.g., root finding, Gaussian elimination, interpolation) are presented. Some novel tools for assignment and grading tasks based on the iPython notebook interface are also presented and discussed.
Speaker: Alessandro Antonucci (IDSIA)
Where: Galleria 1, 2nd floor, Room 204 (SUPSI-DTI, Manno)
Thursday June 27th, 12:00-13:00
Probabilistic reconciliation of hierarchies: a Bayesian approach
When time series are organized into hierarchies, the forecasts have to satisfy some summing constraints. Forecasts which are independently generated for each time series (base forecasts) do not satisfy the constraints. Reconciliation algorithms adjust the base forecast in order to satisfy the summing constraints: in general they also improve the accuracy. We present a novel reconciliation algorithm based on Bayes' rule; we discuss under which assumptions it is optimal and we show in extensive experiments that it compares favorably to the state-of-the-art reconciliation methods
Giorgio Corani is senior researcher at IDSIA; his research interests include probabilistic graphical models, Bayesian methods, and time series.
Wednesday, 26th of June 2019, 12:00-13:00
Manno, Galleria 1, 2nd floor, room G1-201
Pizza and drinks will be offered at the end of the talk. If you plan to attend, please register in a timely fashion at the following link so that we will have no shortage of food:
*Title: PULP-DroNet: Open Source and Open Hardware Artificial
Intelligence for Fully Autonomous Navigation on Nano-UAVs
Nano-size unmanned aerial vehicles (UAVs), with few centimeters of
diameter and sub-10 Watts of total power budget, have so far been
considered incapable of running sophisticated visual-based autonomous
navigation software without external aid from base-stations, ad-hoc
local positioning infrastructure, and powerful external computation
In this talk, we present what is, to the best of our knowledge, the
first 27g nano-UAV system able to run aboard an end-to-end,
closed-loop visual pipeline for autonomous navigation based on a
state-of-the-art deep-learning algorithm, built upon the open-source
Crazyflie 2.0 nano-quadrotor. Our visual navigation engine is enabled
by the combination of an ultra-low power computing device (the GAP8
system-on-chip) with a novel methodology for the deployment of deep
convolutional neural networks (CNNs). We enable onboard real-time
execution of the DroNet state-of-the-art deep CNN at 6
frame-per-second within 64mW and up to 18fps while still consuming on
average just 3.5% of the power envelope of the deployed nano-aircraft.
Field experiments demonstrate that the system's high responsiveness
prevents collisions with unexpected dynamic obstacles up to a flight
speed of 1.5m/s. In addition, we also demonstrate the capability of
our visual navigation engine of fully autonomous indoor navigation on
a 113m previously unseen path.
D-ITET - ETH
Friday June 7th, 12:00-13:00
*Location: Manno, Galleria 1, 2nd floor, room to be announced
Pizza and drinks will be offered at the end of the talk. If you plan
to attend, please register in a timely fashion at the following link
so that we will have no shortage of food:
Application of Deep Learning to Metabolomics: improved steroid identification via deep learning retention time predictions and high-resolution GCxGC-MS
The untargeted steroid identification represents an important analytical challenge due to the chemical similarity of the molecules. Moreover, new experimental technologies such as the two-dimensional gas chromatography (GCxGC) coupled with high resolution time of fly mass spectrometry (HRMS-TOF) were demonstrated to show superior separation power especially for the isomeric compound discrimination. Unfortunately, few molecules are generally annotated, limiting thus the comprehension of the steroid metabolism in its complexity. To overcome this current limitation, in-silico retention time predictions represent an interesting option.
In this work, several machine learning and deep learning algorithms were utilised for the development of retention time prediction models in GCxGC. Starting from a three-dimensional molecular representation, convolutional neural networks (CNN) showed the best prediction performances compared to the classical machine learning models based on handcrafted molecular descriptors. Moreover, CNN were demonstrated to recognize the chiral information and to solve an important issue for steroid identification without the need for a manual feature engineering.
The final prediction model is applied to a real clinical case study. In combination with the MS information, retention time predictions allowed the untargeted annotation of 12 steroids in the urine of new-borns.
Giuseppe Marco Randazzo, PhD
IDSIA - Computational Biophysics Group - Prof. Andrea Danani
Wednesday, 12th of June 2019, 12:00-13:00
*Location: Manno, Galleria 1, 2nd floor, room G1-201
Pizza and drinks will be offered at the end of the talk. If you plan to attend, please register in a timely fashion at the
following link so that we will have no shortage of food:
Numerics and quantum physics: a stochastic computational method for cold atoms in optical lattices.
At very small scale, of the order of the nanometer, classical physics becomes insufficient for describing matter, because quantum effects emerge prominently. Studying this physics can be a very challenging task, both experimentally and theoretically, because of the complexity of matter itself. For this reason, in 1982, Richard Feynman proposed not to study matter directly, but to simulate it, using a so-called quantum simulator [Int. J. Theor. Physics, 21:467]. This would amount to studying some “simple” experimental quantum systems, which can be mapped onto more complex ones. More than twenty years later, thanks to great technological advances, the existence of quantum simulators was made possible. In this talk we will focus on a specific class of quantum simulators, namely cold atoms in optical lattices, which constitute a highly controllable experimental setup for the study of quantum effects. Despite their controllability, due to their quantum nature, an exact mathematical study of these systems remains inaccessible to the computational capacity of current technology. For this reason most approaches rely either on approximations or on stochastic methods. We will present a new computational approach based both on an approximation and a stochastic (Monte Carlo) method. At the end of the talk we will also briefly review the relations between quantum physics and machine learning.