Learning Visual Reasoning Without Strong Priors

Achieving artificial visual reasoning - the ability to answer image-related questions which require a multi-step, high-level process - is an important step towards artificial general intelligence. This multi-modal task requires learning a question-dependent, structured reasoning process over images from language. Standard deep learning approaches tend to exploit biases in the data rather than learn this underlying structure, while leading methods learn to visually reason successfully but are hand-crafted for reasoning. We show that a general-purpose, Conditional Batch Normalization approach achieves state-of-the-art results on the CLEVR Visual Reasoning benchmark with a 2.4% error rate. We outperform the next best end-to-end method (4.5%) and even methods that use extra supervision (3.1%). We probe our model to shed light on how it reasons, showing it has learned a question-dependent, multi-step process. Previous work has operated under the assumption that visual reasoning calls for a specialized architecture, but we show that a general architecture with proper conditioning can learn to visually reason effectively.Comment: Full AAAI 2018 paper is at arXiv:1709.07871. Presented at ICML 2017's Machine Learning in Speech and Language Processing Workshop. Code is at http://github.com/ethanjperez/fil

FiLM: Visual Reasoning with a General Conditioning Layer

We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network computation via a simple, feature-wise affine transformation based on conditioning information. We show that FiLM layers are highly effective for visual reasoning - answering image-related questions which require a multi-step, high-level process - a task which has proven difficult for standard deep learning methods that do not explicitly model reasoning. Specifically, we show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are robust to ablations and architectural modifications, and 4) generalize well to challenging, new data from few examples or even zero-shot.Comment: AAAI 2018. Code available at http://github.com/ethanjperez/film . Extends arXiv:1707.0301

HoME: a Household Multimodal Environment

We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning, and more. We hope HoME better enables artificial agents to learn as humans do: in an interactive, multimodal, and richly contextualized setting.Comment: Presented at NIPS 2017's Visually-Grounded Interaction and Language Worksho

Actionable Intelligence-Oriented Cyber Threat Modeling Framework

Amid the growing challenges of cybersecurity, the new paradigm of cyber threat intelligence (or CTI) has gained momentum to better deal with cyber threats. There, however, has been one fundamental and very practical problem of information overload organizations face in constructing an effective CTI program. We developed a cyber threat intelligence prototype that automatically and dynamically performs the correlation of business assets, vulnerabilities, and cyber threat information in a scoped setting to remediate the challenge of information overload. Conveniently called TIME (for Threat Intelligence Modeling Environment), it repeats the cycle of: (1) collect internal asset data; (2) gather vulnerability and threat data; (3) correlate vulnerabilities with assets; and (4) derive CTI and alerts significant internal asset-related vulnerabilities in a timely manner. For this, it takes advantage of CTI reports produced by online sites and several NIST standards intended to formalize vulnerability and threat management

Advancing Immigrant Incorporation in Austin, TX

Austin is one of the fastest growing cities in the United States and is identified as an emerging gateway for immigrants. The single largest source country for immigrants to Austin continues to be Mexico, but immigrants from Asia are increasing in numbers and relative proportion. Immigrants from Africa doubled over the past decade and make up 4 percent of the foreign-born population. In other words, Austin’s foreign-born residents are increasingly diverse. This report serves to inform the City of Austin as it advances its immigrant incorporation efforts and focuses on five policy areas that the City of Austin can advance: 1) leadership and governance; 2) civic engagement and inclusivity; 3) economic prosperity and job growth; 4) livability; and 5) community resilience.LBJ School of Public Affair

La influencia de la improvisación musical sobre las habilidades técnico-interpretativas en violinistas de formación clásica. Recomendaciones de materiales para los estudiantes de violín de la Especialidad de Música de la Pontificia Universidad Católica del Perú

La improvisación musical es la creación espontánea de una línea melódica bajo un contexto armónico-rítmico determinado. La práctica de la improvisación desarrolla habilidades relacionadas con la creatividad, la espontaneidad y el oído armónico. A pesar de estos beneficios y según los resultados de la encuesta realizada, improvisar no forma parte de la práctica regular de los violinistas de la Especialidad de Música de la Pontificia Universidad Católica del Perú. Por ese motivo, la presente investigación tiene como objetivo general identificar de qué manera la improvisación musical influye sobre las habilidades técnicointerpretativas en violinistas de formación clásica. Además, se incluye una propuesta de materiales que permita introducir la improvisación en la formación de los estudiantes de violín de la Especialidad de Música de la Pontificia Universidad Católica del Perú. En la metodología se presenta un enfoque cualitativo descriptivo, a partir de la revisión bibliográfica y la entrevista semiestructurada a dos maestros violinistas improvisadores: Kostia Lukyniuk y Joshue Ashby. Además, se aplicó una encuesta a los violinistas de la Especialidad de Música de la PUCP, que permitió diseñar una propuesta de materiales de acuerdo con sus intereses y necesidades. Finalmente se realizó una selección de materiales ordenados para el acercamiento a la improvisación en el violín, que fueron ejecutados y analizados a través de sesiones de práctica auto-observada y documentada para sustentar la propuesta en la experiencia personal. Los resultados muestran que la improvisación beneficia al desarrollo de habilidades técnico-interpretativas relacionadas, fundamentalmente, con la producción del sonido, la ejecución de base armónica, los golpes de arco, el dominio de la afinación, el reconocimiento del diapasón, la espontaneidad y la creatividad