Queer (In)equality: an In-depth Look on Discrimination Towards the LGB Community

Table of Contents: A History of Discrimination Against LGB People ; Military Personnel and the LGB Spectrum ; Police Brutality and LGBT Human Rights Violations ; Workplace Discrimination and the LGB Identity ; Homelessness in LGB Adolescents ; Marriage Equality: The Comprehensive Solution; Closing Remarks

CS in HS: Promoting Computer Science Education in High School

The world is in the midst of a technology revolution. Each day, new computing devices are introduced, hundreds of new websites are created, and people who have never used the Internet are trying it out for the first time. Nearly two thirds of Americans currently own a smartphone, and that number will only continue to climb (Fingas, 2014). Even cars, thermostats and refrigerators are becoming computerized and connected. This isn\u27t groundbreaking information; most people are aware of this. What isn’t common knowledge, however, is who creates this technology. How does Google always seem to know exactly what you are asking for, even when you’re not sure how to ask it? How does Facebook manage to accommodate the various needs and interests of over 1 billion users each month (Protalinsk, 2014)? Who is responsible for making this technology so comprehensive? The answer to the last question, of course, is computer scientists, software engineers, database administrators, computer engineers and others in the technology industry. These computing jobs have shaped the modern world in ways only science fiction could have predicted. These people are responsible for some of the greatest innovations of the last century. They work every day on technology that is shaping the future. However, it is the future that is looking bleak. There are currently too few college graduates suited to fill the demand of the growing tech industry

Deformable Part Models are Convolutional Neural Networks

Deformable part models (DPMs) and convolutional neural networks (CNNs) are two widely used tools for visual recognition. They are typically viewed as distinct approaches: DPMs are graphical models (Markov random fields), while CNNs are "black-box" non-linear classifiers. In this paper, we show that a DPM can be formulated as a CNN, thus providing a novel synthesis of the two ideas. Our construction involves unrolling the DPM inference algorithm and mapping each step to an equivalent (and at times novel) CNN layer. From this perspective, it becomes natural to replace the standard image features used in DPM with a learned feature extractor. We call the resulting model DeepPyramid DPM and experimentally validate it on PASCAL VOC. DeepPyramid DPM significantly outperforms DPMs based on histograms of oriented gradients features (HOG) and slightly outperforms a comparable version of the recently introduced R-CNN detection system, while running an order of magnitude faster

Learning Features by Watching Objects Move

This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation. Specifically, we use unsupervised motion-based segmentation on videos to obtain segments, which we use as 'pseudo ground truth' to train a convolutional network to segment objects from a single frame. Given the extensive evidence that motion plays a key role in the development of the human visual system, we hope that this straightforward approach to unsupervised learning will be more effective than cleverly designed 'pretext' tasks studied in the literature. Indeed, our extensive experiments show that this is the case. When used for transfer learning on object detection, our representation significantly outperforms previous unsupervised approaches across multiple settings, especially when training data for the target task is scarce.Comment: CVPR 201

Learning to Segment Every Thing

Most methods for object instance segmentation require all training examples to be labeled with segmentation masks. This requirement makes it expensive to annotate new categories and has restricted instance segmentation models to ~100 well-annotated classes. The goal of this paper is to propose a new partially supervised training paradigm, together with a novel weight transfer function, that enables training instance segmentation models on a large set of categories all of which have box annotations, but only a small fraction of which have mask annotations. These contributions allow us to train Mask R-CNN to detect and segment 3000 visual concepts using box annotations from the Visual Genome dataset and mask annotations from the 80 classes in the COCO dataset. We evaluate our approach in a controlled study on the COCO dataset. This work is a first step towards instance segmentation models that have broad comprehension of the visual world

Aerodynamic Interference on Finned Slender Body

Aerodynamic interference can occur between high-speed slender bodies when in close proximity. A complex flowfield develops where shock and expansion waves from a generator body impinge upon the adjacent receiver body and modify its aerodynamic characteristics in comparison to the isolated case. The aim of this research is to quantify and understand the multibody interference effects that arise between a finned slender body and a second disturbance generator body. A parametric wind tunnel study was performed in which the effects of the receiver incidence and axial stagger were considered. Computational fluid dynamic simulations showed good agreement with the measurements, and these were used in the interpretation of the experimental results. The overall interference loads for a given multibody configuration were found to be a complex function of the pressure footprints from the compression and expansion waves emanating from the generator body as well as the flow pitch induced by the generator shockwave. These induced interference loads change sign as the shock impingement location moves aft over the receiver and in some cases cause the receiver body to become statically unstable. Overall, the observed interference effects can modify the subsequent body trajectories and may increase the likelihood of a collision

A model for optimization of the productivity and bioremediation efficiency of marine integrated multitrophic aquaculture

Integrated multitrophic aquaculture (IMTA) has been proposed as a solution to nutrient enrichment generated by intensive fish mariculture. In order to evaluate the potential of IMTA as a nutrient bioremediation method it is essential to know the ratio of fed to extractive organisms required for the removal of a given proportion of the waste nutrients. This ratio depends on the species that compose the IMTA system, on the environmental conditions and on production practices at a target site. Due to the complexity of IMTA the development of a model is essential for designing efficient IMTA systems. In this study, a generic nutrient flux model for IMTA was developed and used to assess the potential of IMTA as a method for nutrient bioremediation. A baseline simulation consisting of three growth models for Atlantic salmon Salmo salar, the sea urchin Paracentrotus lividus and for the macroalgae Ulva sp. is described. The three growth models interact with each other and with their surrounding environment and they are all linked via processes that affect the release and assimilation of particulate organic nitrogen (PON) and dissolved inorganic nitrogen (DIN). The model forcing functions are environmental parameters with temporal variations that enables investigation of the understanding of interactions among IMTA components and of the effect of environmental parameters. The baseline simulation has been developed for marine species in a virtually closed system in which hydrodynamic influences on the system are not considered. The model can be used as a predictive tool for comparing the nitrogen bioremediation efficiency of IMTA systems under different environmental conditions (temperature, irradiance and ambient nutrient concentration) and production practices, for example seaweed harvesting frequency, seaweed culture depth, nitrogen content of feed and others, or of IMTA systems with varying combinations of cultured species and can be extended to open water IMTA once coupled with waste distribution models

Investigation of a novel approach for aquaculture site selection

This study investigated the potential use of two “species distribution models” (SDMs), Mahalanobis Typicality and Maxent, for aquaculture site selection. SDMs are used in ecological studies to predict the spatial distribution of species based on analysis of conditions at locations of known presence or absence. Here the input points are aquaculture sites, rather than species occurrence, thus the models evaluate the parameters at the sites and identify similar areas across the rest of the study area. This is a novel approach that avoids the need for data reclassification and weighting which can be a source of conflict and uncertainty within the commonly used multi-criteria evaluation (MCE) technique. Using pangasius culture in the Mekong Delta, Vietnam, as a case study, Mahalanobis Typicality and Maxent SDMs were evaluated against two models developed using the MCE approach. Mahalanobis Typicality and Maxent assess suitability based on similarity to existing farms, while the MCE approach assesses suitability using optimal values for culture. Mahalanobis Typicality considers the variables to have equal importance whereas Maxent analyses the variables to determine those which influence the distribution of the input data. All of the models indicate there are suitable areas for culture along the two main channels of the Mekong River which are currently used to farm pangasius and also inland in the north and east of the study area. The results show the Mahalanobis Typicality model had more high scoring areas and greater overall similarity than Maxent to the MCE outputs, suggesting, for this case study, it was the most appropriate SDM for aquaculture site selection. With suitable input data, a combined SDM and MCE model would overcome limitations of the individual approaches, allowing more robust planning and management decisions for aquaculture, other stakeholders and the environment