Guided Open Vocabulary Image Captioning with Constrained Beam Search

Existing image captioning models do not generalize well to out-of-domain images containing novel scenes or objects. This limitation severely hinders the use of these models in real world applications dealing with images in the wild. We address this problem using a flexible approach that enables existing deep captioning architectures to take advantage of image taggers at test time, without re-training. Our method uses constrained beam search to force the inclusion of selected tag words in the output, and fixed, pretrained word embeddings to facilitate vocabulary expansion to previously unseen tag words. Using this approach we achieve state of the art results for out-of-domain captioning on MSCOCO (and improved results for in-domain captioning). Perhaps surprisingly, our results significantly outperform approaches that incorporate the same tag predictions into the learning algorithm. We also show that we can significantly improve the quality of generated ImageNet captions by leveraging ground-truth labels.Comment: EMNLP 201

Research pressure instrumentation for NASA Space Shuttle main engine, modification no. 6

Research concerning the utilization of silicon piezoresistive strain sensing technology for space shuttle main engine applications is reported. The following specific topics were addressed: (1) transducer design and materials, (2) silicon piezoresistor characterization at cryogenic temperatures, (3) chip mounting characterization, and (4) frequency response optimization

The natural history of bugs: using formal methods to analyse software related failures in space missions

Space missions force engineers to make complex trade-offs between many different constraints including cost, mass, power, functionality and reliability. These constraints create a continual need to innovate. Many advances rely upon software, for instance to control and monitor the next generation ‘electron cyclotron resonance’ ion-drives for deep space missions.Programmers face numerous challenges. It is extremely difficult to conduct valid ground-based tests for the code used in space missions. Abstract models and simulations of satellites can be misleading. These issues are compounded by the use of ‘band-aid’ software to fix design mistakes and compromises in other aspects of space systems engineering. Programmers must often re-code missions in flight. This introduces considerable risks. It should, therefore, not be a surprise that so many space missions fail to achieve their objectives. The costs of failure are considerable. Small launch vehicles, such as the U.S. Pegasus system, cost around $18 million. Payloads range from$4 million up to $1 billion for security related satellites. These costs do not include consequent business losses. In 2005, Intelsat wrote off$73 million from the failure of a single uninsured satellite. It is clearly important that we learn as much as possible from those failures that do occur. The following pages examine the roles that formal methods might play in the analysis of software failures in space missions

Stochastic Sampling Simulation for Pedestrian Trajectory Prediction

Urban environments pose a significant challenge for autonomous vehicles (AVs) as they must safely navigate while in close proximity to many pedestrians. It is crucial for the AV to correctly understand and predict the future trajectories of pedestrians to avoid collision and plan a safe path. Deep neural networks (DNNs) have shown promising results in accurately predicting pedestrian trajectories, relying on large amounts of annotated real-world data to learn pedestrian behavior. However, collecting and annotating these large real-world pedestrian datasets is costly in both time and labor. This paper describes a novel method using a stochastic sampling-based simulation to train DNNs for pedestrian trajectory prediction with social interaction. Our novel simulation method can generate vast amounts of automatically-annotated, realistic, and naturalistic synthetic pedestrian trajectories based on small amounts of real annotation. We then use such synthetic trajectories to train an off-the-shelf state-of-the-art deep learning approach Social GAN (Generative Adversarial Network) to perform pedestrian trajectory prediction. Our proposed architecture, trained only using synthetic trajectories, achieves better prediction results compared to those trained on human-annotated real-world data using the same network. Our work demonstrates the effectiveness and potential of using simulation as a substitution for human annotation efforts to train high-performing prediction algorithms such as the DNNs.Comment: 8 pages, 6 figures and 2 table

Stochastic Sampling Simulation for Pedestrian Trajectory Prediction

Urban environments pose a significant challenge for autonomous vehicles (AVs) as they must safely navigate while in close proximity to many pedestrians. It is crucial for the AV to correctly understand and predict the future trajectories of pedestrians to avoid collision and plan a safe path. Deep neural networks (DNNs) have shown promising results in accurately predicting pedestrian trajectories, relying on large amounts of annotated real-world data to learn pedestrian behavior. However, collecting and annotating these large real-world pedestrian datasets is costly in both time and labor. This paper describes a novel method using a stochastic sampling-based simulation to train DNNs for pedestrian trajectory prediction with social interaction. Our novel simulation method can generate vast amounts of automatically-annotated, realistic, and naturalistic synthetic pedestrian trajectories based on small amounts of real annotation. We then use such synthetic trajectories to train an off-the-shelf state-of-the-art deep learning approach Social GAN (Generative Adversarial Network) to perform pedestrian trajectory prediction. Our proposed architecture, trained only using synthetic trajectories, achieves better prediction results compared to those trained on human-annotated real-world data using the same network. Our work demonstrates the effectiveness and potential of using simulation as a substitution for human annotation efforts to train high-performing prediction algorithms such as the DNNs.Comment: 8 pages, 6 figures and 2 table

Inferences on Criminality Based on Appearance

In our research study, we tested whether people can tell if someone is a criminal or not based on a photograph of their face. The importance of the subject lies in the fact that many people are unfairly judged as criminals based on stereotypes such as race. In this study, we wished to eliminate race and see if any purely facial characteristics are stereotypically defined as criminal or if a person’s initial judgment is an accurate predictor of someone’s character. Extensive research has been dedicated to finding if people have facial features that portray some characteristic about them and this study will focus on criminality. Through the use of a face modulating program, neutral faced photographs were shown to participants with a question that asked if the person in the photograph is a criminal or not. The data gathered will be beneficial in either identifying facial features that are associated with criminals or that show the interesting phenomena of gut instinct

Induction and repression of mammalian achaete-scute homologue (MASH) gene expression during neuronal differentiation of P19 embryonal carcinoma cells

MASH1 and MASH2, mammalian homologues of the Drosophila neural determination genes achaete-scute, are members of the basic helix-loop-helix (bHLH) family of transcription factors. We show here that murine P19 embryonal carcinoma cells can be used as a model system to study the regulation and function of these genes. MASH1 and MASH2 display complementary patterns of expression during the retinoic-acid-induced neuronal differentiation of P19 cells. MASH1 mRNA is undetectable in undifferentiated P19 cells but is induced to high levels by retinoic acid coincident with neuronal differentiation. In contrast, MASH2 mRNA is expressed in undifferentiated P19 cells and is repressed by retinoic acid treatment. These complementary expression patterns suggest distinct functions for MASH1 and MASH2 in development, despite their sequence homology. In retinoic-acid-treated P19 cells, MASH1 protein expression precedes and then overlaps expression of neuronal markers. However, MASH1 is expressed by a smaller proportion of cells than expresses such markers. MASH1 immunoreactivity is not detected in differentiated cells displaying a neuronal morphology, suggesting that its expression is transient. These features of MASH1 expression are similar to those observed in vivo, and suggest that P19 cells represent a good model system in which to study the regulation of this gene. Forced expression of MASH1 was achieved in undifferentiated P19 cells by transfection of a cDNA expression construct. The transfected cells expressing exogenous MASH1 protein contained E-box-binding activity that could be super-shifted by an anti-MASH1 antibody, but exhibited no detectable phenotypic changes. Thus, unlike myogenic bHLH genes, such as MyoD, which are sufficient to induce muscle differentiation, expression of MASH1 appears insufficient to promote neurogenesis