Interpretable Compositional Representations for Robust Few-Shot Generalization.

We propose Recognition as Part Composition (RPC), an image encoding approach inspired by human cognition. It is based on the cognitive theory that humans recognize complex objects by components, and that they build a small compact vocabulary of concepts to represent each instance with. RPC encodes images by first decomposing them into salient parts, and then encoding each part as a mixture of a small number of prototypes, each representing a certain concept.

Machine learning combining CT findings and clinical parameters improves prediction of length of stay and ICU admission in torso trauma.

Objective: To develop machine learning (ML) models capable of predicting ICU admission and extended length of stay (LOS) after torso (chest, abdomen, or pelvis) trauma, by using clinical and/or imaging data.

Materials And Methods: This was a retrospective study of 840 adult patients admitted to a level 1 trauma center after injury to the torso over the course of 1 year. Clinical parameters included age, sex, vital signs, clinical scores, and laboratory values.

Unique contributions of parvalbumin and cholinergic interneurons in organizing striatal networks during movement.

Striatal parvalbumin (PV) and cholinergic interneurons (CHIs) are poised to play major roles in behavior by coordinating the networks of medium spiny cells that relay motor output. However, the small numbers and scattered distribution of these cells have hindered direct assessment of their contribution to activity in networks of medium spiny neurons (MSNs) during behavior. Here, we build on recent improvements in single-cell calcium imaging combined with optogenetics to test the capacity of PVs and CHIs to affect MSN activity and behavior in mice engaged in voluntary locomotion.

Mild Blast Injury Produces Acute Changes in Basal Intracellular Calcium Levels and Activity Patterns in Mouse Hippocampal Neurons.

Mild traumatic brain injury (mTBI) represents a serious public health concern. Although much is understood about long-term changes in cell signaling and anatomical pathologies associated with mTBI, little is known about acute changes in neuronal function. Using large scale Ca imaging in vivo, we characterized the intracellular Ca dynamics in thousands of individual hippocampal neurons using a repetitive mild blast injury model in which blasts were directed onto the cranium of unanesthetized mice on two consecutive days.

Sequential Optimization for Efficient High-Quality Object Proposal Generation.

We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING [1] but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially.

Prediction of hospitalization due to heart diseases by supervised learning methods.

Background: In 2008, the United States spent $2.2 trillion for healthcare, which was 15.5% of its GDP.

Behavior subtraction.

Background subtraction has been a driving engine for many computer vision and video analytics tasks. Although its many variants exist, they all share the underlying assumption that photometric scene properties are either static or exhibit temporal stationarity. While this works in many applications, the model fails when one is interested in discovering changes in scene dynamics instead of changes in scene's photometric properties; the detection of unusual pedestrian or motor traffic patterns are but two examples.

Activity based matching in distributed camera networks.

In this paper, we consider the problem of finding correspondences between distributed cameras that have partially overlapping field of views. When multiple cameras with adaptable orientations and zooms are deployed, as in many wide area surveillance applications, identifying correspondence between different activities becomes a fundamental issue. We propose a correspondence method based upon activity features that, unlike photometric features, have certain geometry independence properties.