Phil 1.29.19

7:00 – 5:30 ASRC IRAD

  • Theories of Error Back-Propagation in the Brain
    • This review article summarises recently proposed theories on how neural circuits in the brain could approximate the error back-propagation algorithm used by artificial neural networks. Computational models implementing these theories achieve learning as efficient as artificial neural networks, but they use simple synaptic plasticity rules based on activity of presynaptic and postsynaptic neurons. The models have similarities, such as including both feedforward and feedback connections, allowing information about error to propagate throughout the network. Furthermore, they incorporate experimental evidence on neural connectivity, responses, and plasticity. These models provide insights on how brain networks might be organised such that modification of synaptic weights on multiple levels of cortical hierarchy leads to improved performance on tasks.
  • Interactive Machine Learning by Visualization: A Small Data Solution
    • Machine learning algorithms and traditional data mining process usually require a large volume of data to train the algorithm-specific models, with little or no user feedback during the model building process. Such a “big data” based automatic learning strategy is sometimes unrealistic for applications where data collection or processing is very expensive or difficult, such as in clinical trials. Furthermore, expert knowledge can be very valuable in the model building process in some fields such as biomedical sciences. In this paper, we propose a new visual analytics approach to interactive machine learning and visual data mining. In this approach, multi-dimensional data visualization techniques are employed to facilitate user interactions with the machine learning and mining process. This allows dynamic user feedback in different forms, such as data selection, data labeling, and data correction, to enhance the efficiency of model building. In particular, this approach can significantly reduce the amount of data required for training an accurate model, and therefore can be highly impactful for applications where large amount of data is hard to obtain. The proposed approach is tested on two application problems: the handwriting recognition (classification) problem and the human cognitive score prediction (regression) problem. Both experiments show that visualization supported interactive machine learning and data mining can achieve the same accuracy as an automatic process can with much smaller training data sets.
  • Shifted Maps: Revealing spatio-temporal topologies in movement data
    • We present a hybrid visualization technique that integrates maps into network visualizations to reveal and analyze diverse topologies in geospatial movement data. With the rise of GPS tracking in various contexts such as smartphones and vehicles there has been a drastic increase in geospatial data being collect for personal reflection and organizational optimization. The generated movement datasets contain both geographical and temporal information, from which rich relational information can be derived. Common map visualizations perform especially well in revealing basic spatial patterns, but pay less attention to more nuanced relational properties. In contrast, network visualizations represent the specific topological structure of a dataset through the visual connections of nodes and their positioning. So far there has been relatively little research on combining these two approaches. Shifted Maps aims to bring maps and network visualizations together as equals. The visualization of places shown as circular map extracts and movements between places shown as edges, can be analyzed in different network arrangements, which reveal spatial and temporal topologies of movement data. We implemented a web-based prototype and report on challenges and opportunities about a novel network layout of places gathered during a qualitative evaluation.
    • Demo!
  • More TkInter.
    • Starting Modern Tkinter for Busy Python Developers
    • Spent a good deal of time working through how to get an image to appear. There are two issues:
      • Loading file formats:
        from tkinter import *
        from tkinter import ttk
        from PIL import Image, ImageTk
      • This is because python doesn’t know natively how to load much beyond gif, it seems. However, there is the Python Image Library, which does. Since the original PIL is deprecated, install Pillow instead. It looks like the import and bindings are the same.
      • dealing with garbage collection (“self” keeps the pointer alive):
        image = Image.open("hal.jpg")
        self.photo = ImageTk.PhotoImage(image)
        ttk.Label(mainframe, image=self.photo).grid(column=1, row=1, sticky=(W, E))
      • The issue is that if the local variable that contains the reference goes out of scope, the garbage collector (in Tkinter? Not sure) scoops it up before the picture can even appear, causing the system (and the debugger) to try to draw a None. If you make the reference global to the class (i.e. self.xxx), then the reference is maintained and everything works.
    • The relevant stack overflow post.
    • A pretty picture of everything working:
      • app
  • The 8.6.9 Tk/Ttk documentation
  • Looks like there are some WYSIWYG tools for building pages. PyGubu looks like its got the most recent activity
  • Now my app resizes on grid layouts: app2

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.