Abstract
Driving intelligence test is critical to the development
and deployment of autonomous vehicles. The
prevailing approach tests autonomous vehicles in life-
like simulations of the naturalistic driving environment.
However, due to the high dimensionality of the
environment and the rareness of safety-critical events,
hundreds of millions of miles would be required to
demonstrate the safety performance of autonomous
vehicles, which is severely inefficient. We discover that
sparse but adversarial adjustments to the naturalistic driving environment, resulting in the
naturalistic and adversarial driving environment, can significantly reduce the required test
miles without loss of evaluation unbiasedness. By training the background vehicles to
learn when to execute what adversarial maneuver, the proposed environment becomes
an intelligent environment for driving intelligence testing. We demonstrate the
effectiveness of the proposed environment in a highway-driving simulation. Comparing
with the naturalistic driving environment, the proposed environment can accelerate the
evaluation process by multiple orders of magnitude.

ZOOM LINK: Meeting ID: 950 6760 3617; Passcode: 426506
https://stonybrook.zoom.us/j/95067603617?pwd=dXQybEprSkNlTFY3WHlWYjViUG…

Bio
Professor Henry Liu is a professor in the Department of Civil and Environmental
Engineering at the University of Michigan, Ann Arbor. He is also a Research Professor at
the University of Michigan Transportation Research Institute and the Director for the
Center for Connected and Automated Transportation (USDOT Region 5 University
Transportation Center). Prof. Liu conducts interdisciplinary research at the interface
between civil and mechanical engineering. Specifically, his scholarly interests concern
traffic flow monitoring, modeling, and control, as well as testing and evaluation of
connected and automated vehicles. He has published more than 100 refereed journal
papers and is listed as one of the top 50 leading authors in the past 50 years (1969-2019)
in the prestigious Transportation Research journal. Professor Liu and his work have been
widely recognized in public media for promoting smart transportation innovations. He has
appeared on media outlets including CNBC, Forbes, Technode, etc. In 2019, Professor
Liu was invited to testify on national transportation research agenda in front of the US
House Subcommittee on Research and Technology. Professor Liu has nurtured a new
generation of scholars, and some of his PhD students and postdocs have joined first class
universities such as Columbia University, Purdue University, RPI, etc. Prof. Liu is the
managing editor of Journal of Intelligent Transportation Systems.



Date of Event

Joel H. Saltz, MD, PhD
SUNY Distinguished Professor Cherith Professor and Founding Chair
Department of Biomedical Informatics
Stony Brook University

Apostolos K. Tassiopoulos, MD, FACS
Professor of surgery and vice chair for quality and outcomes Chief of the Division of Vascular and Endovascular Surgery
Director of the Stony Brook Vascular Center Stony Brook Medicine

Title: Clinical applications of artificial intelligence to improve diagnosis and risk stratification for patients with aortic aneurysms

Time: Wednesday, Feb 17, 2021 3 pm - 4 pm

Join Zoom Meeting
https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISj...
Meeting ID: 956 1719 7636 Passcode: 924293

Title: Class visual similarity based noisy sample removal in generative Few Shot Learning
Time: Thursday, Feb 4, 11:30am - 1:00pm
Zoom:
https://stonybrook.zoom.us/j/8563646526?pwd=anJna1gzUStXNlNVSUIzdDRUSC9CUT09

Meeting ID: 856 364 6526
Passcode: 203791



Abstract:  

Over the past decade, larger datasets, hardware accelerations, and network architecture improvements have contributed to phenomenal achievements in many tasks of computer

vision. However, in the absence of large datasets, computer vision models struggle to learn

general representations which results in poor performance. Few-shot learning tries to address 

this problem by proposing models which learn from a few examples.


I first give an overall review of few-shot learning methods. I particularly focus on generative Few Shot Learning(FSL) methods, which augment the scarce categories in a dataset by generating samples for those rare categories. As the actual class distribution can be complex and lie very close to each other, the sample generated for one class can be noisy or lie close to another class.  However, none of the current FS generative methods perform any form of quality control of the generated samples.


In this work, I propose to identify and remove the generated samples that are less likely to be in the distribution of the few-shot class. Here I particularly deal with few-shot scenarios where the

prior information of the relationship between the classes based on visual  similarity is available. The main idea is to exploit these priors to better identify the unreliable generated samples.


Particularly, I have proposed two methods based on class relationship to detect noisy generated samples. In the first method, we assume that the embedding space of each class follows a Gaussian distribution.  From this assumption, I propose Gaussian Neighborhood (GN), a method to estimate how likely a generated sample is drawn from the estimated distribution of a few-shot class.  We evaluate this method on the Hematopoiesis dataset.  By simply eliminating samples based on thresholding our proposed GN scores, the few-shot  classification  performance  is  improved by 5% and 2% in five shot and one shot respectively, compared to the model trained on all generated images. 


The GN scores represent the similarity distances from the generated samples to their classes, based on the assumption that each class is a Gaussian distribution.  However, this assumption might be strict in many scenarios since the real distributions of data can be arbitrarily complex.  Thus in my second proposed method, I aim to learn such similarity distances directly from data via metric learning. I propose to train a deep-network to regress the similarity distance between a pair of samples.  This network is trained using both the class-level  visual  similarity  information  and  the  class  labels.   This method improves the 1-shot and 5-shot classification performances by 0.5% and 1% respectively, compared to GN.

Jerome Liang, PhD 

Professor of Radiology, Biomedical Engineering, Electric and Computer Engineering, and Computer Science 

Co-Director of Research 

Department of Radiology 


Artificial intelligence, machine learning and computer-aided diagnosis in cancer Imaging 

February 11, 2021 

12:00pm - 1:00pm 

Virtual Seminar - Zoom 

https://stonybrook.zoom.us/j/98155629970?pwd=YzRvcnJnTlNTT1E5ak1oZEJvWTZHQT09 

Meeting ID: 981 5562 9970 

Passcode: 950410 

Host: 

Wei Zhao, PhD 

Professor of Radiology and Biomedical Engineering 

Educational Objectives  

Upon completion, participants should be able to:  

(1) Learn different medical image representations of cancer attributes, such as heterogeneity, high tendency to grow, etc.  

(2) Learn how computer (machine) can be trained (or programmed) to recognize the image representations.  

(3) Learn how artificial intelligence can drive the machine learning to maximize the performance of computer-aided diagnosis (CADx).  

Disclosure Statement  

In compliance with the ACCME Standards for Commercial Support, everyone who is in a position to control the content of an educational activity provided by the School of Medicine is expected to disclose to the audience any relevant financial relationships with any commercial interest that relates to the content of his/her presentation.  

 

The speaker, Jerome Liang, PhD, the planners; and the CME provider have no relevant financial relationship with a commercial interest (defined as any entity producing, marketing, re-selling, or distributing health care goods or services consumed by, or used on, patients), that relates to the content that will be discussed in the educational activity.  

 

CONTINUING MEDICAL EDUCATION CREDITS  

The School of Medicine, State University of New York at Stony Brook, is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.  

 

The School of Medicine, State University of New York at Stony Brook designates this live activity for a maximum of 1.0 AMA PRA Category 1 Credits™. Physicians should only claim credit commensurate with the extent of their participation in the activity.  

 

Should you be logging in Zoom by using your tablet or mobile device, please be sure to add your Full Name and/or Email for CME credit. 


Time: Jan 26, 2021 03:00 PM Eastern Time (US and Canada)

All are welcome!

Zoom Meeting:
https://stonybrook.zoom.us/j/93818552212?pwd=ajZkT2x4a2tiaDJUL1h3VFhLZEgwQT09

Meeting ID: 938 1855 2212
Passcode: 802722

Title: Data-Driven Document Unwarping

Abstract: Capturing document images is a common way to digitize and record physical documents due to the ubiquitousness of mobile cameras. To make text recognition easier, it is often desirable to digitally flatten a document image when the physical document sheet is folded or curved. However, unwarping a document from a single image in natural scenes is very challenging due to the complexity of document sheet deformation, document texture, and environmental conditions. Previous model-driven approaches struggle with inefficiency and limited generalizability. In this thesis, I investigate several data-driven approaches to tackle the document unwarping problem.

Data acquisition is the central challenge in data-driven methods. I first design an efficient data synthesis pipeline based on 2D image warping and train DocUNet, the pioneering data-driven document unwarping model, on the synthetic data. A benchmark dataset is also created to facilitate comprehensive evaluation and comparison. To improve the unwarping performance by training on more realistic data, I introduce the Doc3D dataset and DewarpNet. Supervised by 3D shape ground truth in Doc3D, DewarpNet is significantly better than DocUNet. DocUNet and DewarpNet depend on the synthetic data for the ground truth deformation annotation. To exploit the real-world images, I propose PaperEdge, a weakly supervised model trained with in-the-wild document images with easy-to-obtain boundary information. PaperEdge surpasses DewarpNet by utilizing both the synthetic data and weakly annotated real data in the Document In the Wild (DIW) dataset. Finally, I propose to incorporate the 3D physical constraints in training DewarpNet and PaperEdge. The constraints regulate the possible deformations on document papers. I also propose to augment the Doc3D and DIW dataset by introducing an online document segmentation model and better hardware.
























A new virtual seminar series on Games, Decisions, and Networks will start this Friday. The series aims at bringing together researchers working on foundations and applications of games theory, decision theory, and networks from computer science, control, economics and operation research. 





The advisory board for the series comprises Asu Ozdaglar (MIT), Christos Papadimitriou (Columbia), Drew Fudenberg (MIT), Eva Tardos (Cornell), Matthew O. Jackson (Stanford), Ramesh Johari (Stanford), and Tamer Başar (UIUC). 
The first talk will be given by Costantinos Daskalakis (MIT) on January 22nd at noon ET, titled Equilibrium Computation and the Foundations of Deep Learning. Upcoming speakers include


- Rakesh Vohra (Upenn)
- Sanjeev Goyal (Cambridge)
- Aaron Roth (Upenn)
- Aislinn Bohren (Upenn)
- Jason Marden (UCSB)

and more to be added!

Nam Nguyen

4-5pm, Dec 17 2020

https://stonybrook.zoom.us/j/94214254415?pwd=K1VoQml4cFdlVW51VW41dWtid2tJdz09



The molecular mechanisms and functions in complex biological systems
currently remain elusive. Recent high-throughput techniques, such as
next-generation sequencing, have generated a wide variety of
multiomics datasets that enable the identification of biological
functions and mechanisms via multiple facets. However, integrating
these large-scale multiomics data and discovering functional insights
are, nevertheless, challenging tasks. To address these challenges,
machine learning has been broadly applied to analyze multiomics. In
particular, multiview learning is more effective than previous
integrative methods for learning data's heterogeneity and revealing
cross-talk patterns. Although it has been applied to various contexts,
such as computer vision and speech recognition, multiview learning has
not yet been widely applied to biological data--specifically,
multiomics data. Therefore, we have developed a framework called
multiview empirical risk minimization (MV-ERM) for unifying multiview
learning methods (Nguyen, et al., PLoS Computational Biology, 2020).
MV-ERM enables potential applications to understand multiomics
including genomics, transcriptomics, and epigenomics, in an aim to
discover the functional and mechanistic interpretations across omics.
Based on MV-ERM, we have developed the following methods:
ManiNetCluster, Varmole and ECMarker.



(1) ManiNetCluster (Nguyen, et al., BMC Genomics, 2019) is a manifold
learning method which simultaneously aligns and clusters gene networks
(e.g., co-expression) to systematically reveal the links of genomic
function between different phenotypes. Specifically, ManiNetCluster
employs manifold alignment to uncover and match local and non-linear
structures among networks, and identifies cross-network functional
links. We demonstrated that ManiNetCluster better aligns the
orthologous genes from their developmental expression profiles across
model organisms than state-of-the-art methods. This indicates the
potential non-linear interactions of evolutionarily conserved genes
across species in development. Furthermore, we applied ManiNetCluster
to time series transcriptome data measured in the green alga
Chlamydomonas reinhardtii to discover the genomic functions linking
various metabolic processes between the light and dark periods of a
diurnally cycling culture;



(2) Varmole (Nguyen, et al., Bioinformatics, 2020) is an interpretable
deep learning method that simultaneously reveals genomic functions and
mechanisms while predicting phenotype from genotype. In particular,
Varmole embeds multi-omic networks into a deep neural network
architecture and prioritizes variants, genes and regulatory linkages
via biological drop-connect without needing prior feature selections.
With an application to schizophonia, we demonstrate that Varmole
provides an effective alternative for recent statistical methods that
associate functional omic data (e.g. gene expression) with genotype
and phenotype and that link variants to individual genes in population
studies such as genome-wide association study;



(3) ECMarker (Jin*, Nguyen*, et al., Bioinformatics, 2020) is an
interpretable and scalable machine learning model that predicts gene
expression biomarkers for disease phenotypes and simultaneously
reveals underlying regulatory mechanisms. Particularly, ECMarker is
built on the integration of semi- and discriminative- restricted
Boltzmann machines, a neural network model for classification allowing
lateral connections at the input gene layer. With application to the
gene expression data of non-small cell lung cancer (NSCLC) patients,
we found that ECMarker not only achieved a relatively high accuracy
for predicting cancer stages but also identified the biomarker genes
and gene networks implying the regulatory mechanisms in lung cancer
development.



Finally, we propose a novel multiview learning method, Malignomics, to
predict phenotypes from heterogeneous multi-omic features. Malignomics
will first align multi-omic features by deep manifold alignment onto a
common latent space, better predicting nonlinear relationships across
omics. This deep alignment aims to preserve both global consistency
and local smoothness across omics and reveal higher-order nonlinear
interactions (i.e., manifolds) among cross-omic features. Second, it
uses these manifold structures to regularize the classifiers for
predicting phenotypes. This manifold-regularization allows
highlighting cross-omic feature manifolds and prioritizing the
features and interactions for the phenotypes. The prioritized
multi-omic features will further reveal underlying phenotypic
functions and mechanisms and thus enhance the biological
interpretation of Malignomics. We will apply Malignomics to
multi-omics data in neuropsychiatric disorders, and prioritize gene
regulatory networks linking risk variants, regulatory elements, and
genes for the disorders. We will also compare Malignomics with the
state-of-the-arts, and investigate how the manifold regulation will
potentially improve understanding of multi-omics functions and
predicting diseases.



Place:  https://stonybrook.zoom.us/j/99167126152?pwd=TFpEYzM0aFhiOFJxSFJEb1JSS3YyQT09  

Time: 3 PM EST - Dec, 16th, 2020 

Abstract: 

Shadows provide useful cues to analyze visual scenes but also hamper many computer vision algorithms such as image segmentation, object detection, or tracking. For those reasons, shadow detection and shadow removal have been well-studied in computer vision.

Early work on shadow detection and removal focused on physical illumination models of shadows. These methods can express, identify, and remove shadows in a physically plausible manner. However, these models are often hard to optimize and are slow during inference due to their reliance on hand-designed image features. Recently, deep-learning approaches have achieved breakthroughs in performance for both shadow detection and removal. They learn to extract useful features through training while being extremely efficient during inference. However, these models are data-dependent, opaque, and ignore the physical aspects of shadows. Thus they often lack generalization and produce inconsistent results.

We propose incorporating physical illumination constraints of shadows into deep-learning models. These constraints force the networks to more closely follow the physics of shadows, enabling them to systematically and realistically modify shadows in images. For shadow detection, we present a novel Generative Adversarial Network (GAN) based model where the generator learns to generate images with realistic attenuated shadows that can be used to train a shadow detector. For shadow removal, we propose a method that uses deep-networks to estimate the unknown parameters of a shadow image formation model that removes shadows. The system outputs high-quality shadow-free images with little or no image artifacts and achieves state-of-the-art performance in shadow removal when trained on a fully-supervised setting. Moreover, the system is easy to train and constrain since the shadow removal mapping is strictly defined by the simplified illumination model with interpretable parameters. Thus, it can be trained even with a much weaker form of supervision signal. In particular, we show that we can use two sets of patches, shadow and shadow-free, to train our shadow decomposition framework via an adversarial system. These patches are cropped from the shadow images themselves.
Therefore, this is the first deep-learning method for shadow removal that can be trained without any shadow-free images, providing an alternative solution to the paired data dependency issue. The advantage of this training scheme is even more pronounced when tested on a novel domain such as video shadow removal where the method can be fine-tuned on a testing video with only the shadow masks generated by a pre-trained shadow detector and further improves shadow removal results.