Context-Aware Zero-Shot Recognition

We present a novel problem setting in zero-shot learning, zero-shot object recognition and detection in the context. Contrary to the traditional zero-shot learning methods, which simply infers unseen categories by transferring knowledge from the objects belonging to semantically similar seen categories, we aim to understand the identity of the novel objects in an image surrounded by the known objects using the inter-object relation prior. Specifically, we leverage the visual context and the geometric relationships between all pairs of objects in a single image, and capture the information useful to infer unseen categories. We integrate our context-aware zero-shot learning framework into the traditional zero-shot learning techniques seamlessly using a Conditional Random Field (CRF). The proposed algorithm is evaluated on both zero-shot region classification and zero-shot detection tasks. The results on Visual Genome (VG) dataset show that our model significantly boosts performance with the additional visual context compared to traditional methods

Chopper: Partitioning models into 3D-printable parts

3D printing technology is rapidly maturing and becoming ubiquitous. One of the remaining obstacles to wide-scale adoption is that the object to be printed must fit into the working volume of the 3D printer. We propose a framework, called Chopper, to decompose a large 3D object into smaller parts so that each part fits into the printing volume. These parts can then be assembled to form the original object. We formulate a number of desirable criteria for the partition, including assemblability, having few components, unobtrusiveness of the seams, and structural soundness. Chopper optimizes these criteria and generates a partition either automatically or with user guidance. Our prototype outputs the final decomposed parts with customized connectors on the interfaces. We demonstrate the effectiveness of Chopper on a variety of non-trivial real-world objects.National Science Foundation (U.S.) (Grant CCF-1012147)National Science Foundation (U.S.) (Grant IIS-1116296)Intel Corporation (Science and Technology Center for Visual Computing

Early dietary exposures epigenetically program mammary cancer susceptibility through Igf1-mediated expansion of the mammary stem cell compartment

This article belongs to the Collection Insulin-Like Growth Factors in Development, Cancers and Aging.Diet is a critical environmental factor affecting breast cancer risk, and recent evidence shows that dietary exposures during early development can affect lifetime mammary cancer susceptibility. To elucidate the underlying mechanisms, we used our established crossover feeding mouse model, where exposure to a high-fat and high-sugar (HFHS) diet during defined developmental windows determines mammary tumor incidence and latency in carcinogen-treated mice. Mammary tumor incidence is significantly increased in mice receiving a HFHS post-weaning diet (high-tumor mice, HT) compared to those receiving a HFHS diet during gestation (low-tumor mice, LT). The current study revealed that the mammary stem cell (MaSC) population was significantly increased in mammary glands from HT compared to LT mice. Igf1 expression was increased in mammary stromal cells from HT mice, where it promoted MaSC self-renewal. The increased Igf1 expression was induced by DNA hypomethylation of the Igf1 Pr1 promoter, mediated by a decrease in Dnmt3b levels. Mammary tissues from HT mice also had reduced levels of Igfbp5, leading to increased bioavailability of tissue Igf1. This study provides novel insights into how early dietary exposures program mammary cancer risk, demonstrating that effective dietary intervention can reduce mammary cancer incidenceThe research was supported by institutional funding from Texas A&M University and the Discovery Foundatio

One-pot synthesis of Bi-Ni nanowire and nanocable arrays by coelectrodeposition approach

A novel and convenient one-pot electrodeposition approach has been developed for precisely controlled fabrication of large-scale Bi-Ni nanowire and nanocable arrays. Using porous anodic aluminum oxide as a shape-directing template, by simply changing the electrochemical deposition mode, desired Bi-Ni hybrid nanowires and Bi-Ni core-shell nanocables have been obtained in the CV and CC modes, respectively. The structure, morphology, and composition of the as-prepared samples were characterized using X-ray powder diffraction, transmission electron microscopy, elemental mapping, and energy-dispersive X-ray spectrometry