Machine larning

School of Computer Science, Tel-Aviv University

ANeural Computation & Signal Processing Lab (NCSP)

Current Projects

Prof. Nathan Intrator

Updated: February, 2006

Below, is a description of several open projects. MSc or PhD students are welcome to contact me regarding these projects or other projects they would like to discuss. Please also look at current projects in the lab to get a better idea of the topics.

Students The seminar this year will concentrate computational methods which are used in the different projects in the lab. Specifically, we shall concentrate on machine learning, robust statistics and robust modeling in each of the presentations. The background for the specific projects has been presented in talks in 2004-2005 seminars and thus will not be repeated. There will be few guest presentations as well.

Instructor: Prof. Nathan Intrator, Schreiber 221, x7598, Office hours: Wednesday 4-5 or via email

NCSP Past seminars 2004-5 Sem I 2004-5 Sem II

Other relevant seminars: PBC Seminar Eshel Ben Yaakov Eytan Ruppin Ron Shamir

Presentations

Date	Title	Speaker
Nov 02	Organizational meeting	Nathan Intrator
Nov 09	Meeting with students	Nathan Intrator
Nov 10	Visions of language: through a mirage to an oasis	Shimon Edelman
Nov 16	Seismic data analysis	Talmor/Yariv
Nov 23	Mapping Mutations in the HIV RNA	Nimrod Bar
Nov 30	Results on hidden loop discovery and cursive hand writing recognition	Tal Steinhertz
Dec 07	Music Coloring for High Dimensional Data Representation	Eyal Balla
Dec 14	Spectral ICA and Adaptive noise removal in Heart Sounds	Haim Appleboim
Dec 21	Functional Holography of Complex Network Activity	Itai Baruchi
Jan 11	fMRI: Recent advances in DTI	Ofer Pasternak
Jan 25	Segmentation of Heart Sounds	Daniel Gill
Feb 1	Motion Estimation Improves Ultrasound Imaging	Lian Yu
Mar 8	Discovery of Structure	Ben Sandbak
Mar 15	Data Mining Issues	Dan Feldman

	EEG Data analysis	Andrey Zahdunov
	Automatic segmentation of cyclic time series: APEX Cardiogram	Guy Amit

General instructions for seminar presenters

The presentation should have an introductory component that can enable all students understand the background of the seminar. They should then have a methodological component which explains at least a single method that can be used in a variety of applications. Finally, there should be some application results which demonstrate the usefulness of the proposed methods.

In contrast, a review presentation should describe several computational methods which are aimed at addressing a specific problem, together with a clear background of that problem. Preferably some comparison between the methods should be provided.

Abstract of the presentation should be sent to me up to three days before the presentation and a slides up to a day before the presentation.

Abstracts

Visions of language: through a mirage to an oasis Shimon Edelman

Over the past five decades, the conception of language adopted as the overarching theoretical framework by most linguists has been increasingly considered by most of the other involved scientists as irrelevant to the understanding of cognition and the brain. This unfortunate trend appears now to be reversing, due to a series of developments in cognitive linguistics and psychology, and in computer science. In this talk, I shall briefly survey these developments, focusing on language acquisition -- an issue with respect to which the still dominant "innatist" stance in linguistics bears a curious resemblance to the obscurantist doctrine known as "intelligent design."

The algorithm for language acquisition that I shall mention is joint work with Zach Solan, Eytan Ruppin, and David Horn.

Seismic data analysis Talmor/Yariv

The talk describes the final project in the course “Neural Computation”. Information and code for obtaining seismic data will be presented together with the methodology and results which have led to a new global seismic network presentation which includes even location as well as temporal structure of a collection of events.

Mapping Mutations Patterns in the HIV DNA Nimrod Bar

Outline

· HIV introduction

· HIV DNA mutations

· Retrieving and processing the DNA sequence

· From DNA to Amino Acid Mutations.

· The importance and problem of finding mutation patterns

· Recent biological research of mutations – Bayesian networks.

· Applying Branch and Bound techniques for pattern finding

· Future research – Bi-clustering of mutation data.

Recent results on hidden loop discover and cursive hand writing recognition Tal Steinherz

Methods for moving between offline cursive word recognition to pseudo online representation will be discussed. In particular, recent results on hidden loop recovery will be shown.

Music Coloring for high dimensional data representation Eyal Balla

The talk will include overview of the use of music in psycho therapy, and the use of music in viewing high dimensional data in finances and other applications. An introduction to Max/MSP will be given, together with some demos. Finally, an introduction to VST universal music code will be discussed and music effects using VST plug-ins will be presented. The goal is to have colored music provide additional information to the user, for example about his medical condition for the purpose of monitoring and bio-feedback.

Spectral ICA and Adaptive noise removal in Heart Sounds Haim Appleboim

The talk will include overview of ICA and spectral ICA and demonstrate its usefulness for signal conditioning and enhancing from multiple sources as well as noise and artifacts removal.

Functional Holography of Complex Network Activity Itai Baruchi

A functional holography (FH) approach is introduced for analyzing the complex activity of biological networks in the space of functional correlations. Although the activity is often recorded from only part of the nodes, the goal is to decipher the activity of the whole network. This is why the analysis is guided by the "whole in every part" nature of a holograms – a small part of a hologram will generate the whole picture but with lower resolution. The analysis is started by constructing the space of functional correlations from the similarities between the activities of the network components by a special collective normalization, or affinity transformation. Using dimension reduction algorithms like PCA, a connectivity diagram is generated in the 3-dimensional space of the leading eigenvectors of the algorithm. The network components are positioned in the 3-dimenional space by projection on the eigenvectors and connect them with colored lines that represent the similarities. Temporal (causal) information is superimposed by coloring the node's locations according to the temporal ordering of their activities. By this analysis, the existence of hidden manifolds with simple yet characteristic geometrical and topological features in the complex biological activity was discovered from cultured networks to the human brain. These findings could be a consequence of the analysis being consistent with a new holographic principle by which biological networks regulate their complex activity.

Diffusion Weighted MRI - The Beltrami Flow Ofer Pasternak

Diffusion weighted MRI measures the self diffusion of water molecules. The imaging techniques originate with the work of Stejskal and Tanner in 1965, and became popular with the Diffusion Tensor Imaging of Basser, 1994. In order to model the diffusion one has to solve the diffusion equations. The solutions for those equations are simple for homogeneous material and were recently extended to the general case using the Beltrami flow. All imaging techniques to date rely on the simple solution for the diffusion equations which assumes homogeneity. This means that those models are prune to errors on heterogeneous materials, such as complex brain architecture. In this talk I will explain on different existing diffusion models, and their inabilities to model complex brain architecture. I will introduce the Beltrami flow solution to diffusion equation as it was used for image processing, and will show how the Beltrami flow might be used in diffusion imaging context.

Segmentation of Heart Sounds Daniel Gill

Detection and Identification of Heart Sounds Using Homomorphic Envelogram and Self-Organizing Probabilistic Model

This work presents a novel method for automatic detection and identification of heart sounds. Homomorphic filtering is used to obtain a smooth envelogram of the phonocardiogram, which enables a robust detection of events of interest in heart sound signal. Sequences of features extracted from the detected events are used as observations of a hidden Markov model. It is demonstrated that the task of detection and identification of the major heart sounds can be learnt from unlabelled phonocardiograms by an unsupervised training process and without the assistance of any additional synchronizing channels.

Motion Estimation Improves Ultrasound Imaging Lian Yu

Some reading material

Sound analysis

Auditory display of hyperspectral colon tissue images

Singing the Mind Listening

Sound features

Chris Raphael Rhythm changes

Biomedical signals and sensors

Robust measurement of Carotid Heart sound delay

Heart Mechanical and Electrical System

Segmentation of EKG signals

EKG Overview