HP-GAN: Probabilistic 3D human motion prediction via GAN

Predicting and understanding human motion dynamics has many applications, such as motion synthesis, augmented reality, security, and autonomous vehicles. Due to the recent success of generative adversarial networks (GAN), there has been much interest in probabilistic estimation and synthetic data generation using deep neural network architectures and learning algorithms. We propose a novel sequence-to-sequence model for probabilistic human motion prediction, trained with a modified version of improved Wasserstein generative adversarial networks (WGAN-GP), in which we use a custom loss function designed for human motion prediction. Our model, which we call HP-GAN, learns a probability density function of future human poses conditioned on previous poses. It predicts multiple sequences of possible future human poses, each from the same input sequence but a different vector z drawn from a random distribution. Furthermore, to quantify the quality of the non-deterministic predictions, we simultaneously train a motion-quality-assessment model that learns the probability that a given skeleton sequence is a real human motion. We test our algorithm on two of the largest skeleton datasets: NTURGB-D and Human3.6M. We train our model on both single and multiple action types. Its predictive power for long-term motion estimation is demonstrated by generating multiple plausible futures of more than 30 frames from just 10 frames of input. We show that most sequences generated from the same input have more than 50\% probabilities of being judged as a real human sequence. We will release all the code used in this paper to Github

Effects of varying oxygen partial pressure on molten silicon-ceramic substrate interactions

The silicon sessile drop contact angle was measured on hot pressed silicon nitride, silicon nitride coated on hot pressed silicon nitride, silicon carbon coated on graphite, and on Sialon to determine the degree to which silicon wets these substances. The post-sessile drop experiment samples were sectioned and photomicrographs were taken of the silicon-substrate interface to observe the degree of surface dissolution and degradation. Of these materials, silicon did not form a true sessile drop on the SiC on graphite due to infiltration of the silicon through the SiC coating, nor on the Sialon due to the formation of a more-or-less rigid coating on the liquid silicon. The most wetting was obtained on the coated Si3N4 with a value of 42 deg. The oxygen concentrations in a silicon ribbon furnace and in a sessile drop furnace were measured using the protable thoria-yttria solid solution electrolyte oxygen sensor. Oxygen partial pressures of 10 to the minus 7 power atm and 10 to the minus 8 power atm were obtained at the two facilities. These measurements are believed to represent nonequilibrium conditions

Electronic structure and magnetism in two-dimensional hexagonal 5d transition metal carbides, Tan+1Cn (n=1,2,3)

Density functional calculations are used to investigate the electronic structure of two-dimensional 5d tantalum carbides with honeycomb-like lattice structures. We focus on changes in the low-energy bands near the Fermi level with dimensionality. We find that the Ta 5d states dominate, but the extended nature of the wavefunctions makes them weakly correlated. The carbide sheets are prone to long range magnetic order. We evaluate the stability of these states to enhanced electron--electron interactions through a Hubbard U correction. Lastly, we find spin orbit interactions strongly renormalize the band structure for n=2, but play a minor role in n=1 and 3.Comment: 4 pages, 4 figure

The Thermodynamics of Planetary Engineering on the Planet Mars

Mars is a potentially habitable planet given the appropriate planetary engineering efforts. In order to create a habitable environment, the planet must be terraformed, creating quasi-Earth conditions. Benchmarks for minimum acceptable survivable human conditions were set by observing atmospheric pressures and temperatures here on Earth that humans are known to exist in. By observing a positive feedback reaction, it is shown how the sublimation of the volatile southern polar ice cap on Mars can increase global temperatures and pressures to the benchmarks set for minimum acceptable survivable human conditions. Given the degree of uncertainty, utilization of pressure scale heights and the Martin extreme terrain were used to show how less than desirable conditions can still produce results where these benchmarks can be met. Methods for obtaining enough energy to sublimate the southern polar ice cap were reviewed in detail. A new method of using dark, carbonaceous Martian moon material to alter the overall average albedo of the polar ice cap is proposed. Such a method would increase Martian energy efficiency. It is shown that by covering roughly 10% of the Martian polar ice cap with dark carbonaceous material, this required energy can be obtained. Overall contributions include utilization of pressure scale heights at various suggested settlement sites, as well as polar albedo altering as a method of planetary engineering. This project serves as a foundational work for long term solar system exploration and settlement

A content analysis of the media coverage of the 2014 presidential elections in Egypt: the case studies of CBC and Channel 1

Television broadcasting offers a platform for bringing the attention to issues of high importance, and forming opinions on these issues. With a high dependency on television broadcasting in Egypt to receive news and information about current events to formulate opinions, regulated television broadcasting is very crucial to aid the shift towards a more democratic nation. Balanced and impartial reporting is essential to ensure pluralism in the information presented. Hence, this thesis examines the performance of public media compared to privately owned media in light of their coverage of the 2014 presidential elections. The overall theoretical approach is based on Article 19 and UNESCO\u27s standard guidelines for broadcasting during election times. CBC and Channel1 are used as case studies from both media sectors. Qualitative content analysis is used to analyze coverage under both channels, from 3rd May to 23rd May 2014 (the official campaigning period), to mainly assess for balance, fairness, impartiality and comprehensiveness. Current affairs talk shows, as well as news content were covered, on the basis of popularity and relevance to the elections. Furthermore, the research examines policy changes that can be undertaken to ensure better regulation of the coverage of information during election times

Education and skills versus careers and job interviews

This is an interview with an alumnus from the American University in Cairo. It is about education at AUC in relation to work fields

Ti3SiC2-formation during Ti–C–Si multilayer deposition by magnetron sputtering at 650 °C

Titanium Silicon Carbide films were deposited from three separate magnetrons with elemental targets onto Si wafer substrates. The substrate was moved in a circular motion such that the substrate faces each magnetron in turn and only one atomic species (Ti, Si or C) is deposited at a time. This allows layer-by-layer film deposition. Material average composition was determined to Ti0.47Si0.14C0.39 by energy-dispersive X-ray spectroscopy. High-resolution transmission electron microscopy and Raman spectroscopy were used to gain insights into thin film atomic structure arrangements. Using this new deposition technique formation of Ti3SiC2 MAX phase was obtained at a deposition temperature of 650 °C, while at lower temperatures only silicides and carbides are formed. Significant sharpening of Raman E2g and Ag peaks associated with Ti3SiC2 formation was observed

Human Motion Anticipation and Recognition from RGB-D

Predicting and understanding the dynamic of human motion has many applications such as motion synthesis, augmented reality, security, education, reinforcement learning, autonomous vehicles, and many others. In this thesis, we create a novel end-to-end pipeline that can predict multiple future poses from the same input, and, in addition, can classify the entire sequence. Our focus is on the following two aspects of human motion understanding: Probabilistic human action prediction: Given a sequence of human poses as input, we sample multiple possible future poses from the same input sequence using a new GAN-based network. Human motion understanding: Given a sequence of human poses as input, we classify the actual action performed in the sequence and improve the classification performance using the presentation learned from the prediction network. We also demonstrate how to improve model training from noisy labels, using facial expression recognition as an example. More specifically, we have 10 taggers to label each input image, and compare four different approaches: majority voting, multi-label learning, probabilistic label drawing, and cross-entropy loss. We show that the traditional majority voting scheme does not perform as well as the last two approaches that fully leverage the label distribution. We shared the enhanced FER+ data set with multiple labels for each face image with the research community (https://github.com/Microsoft/FERPlus). For predicting and understanding of human motion, we propose a novel sequence-to-sequence model trained with an improved version of generative adversarial networks (GAN). Our model, which we call HP-GAN2, learns a probability density function of future human poses conditioned on previous poses. It predicts multiple sequences of possible future human poses, each from the same input sequence but seeded with a different vector z drawn from a random distribution. Moreover, to quantify the quality of the non-deterministic predictions, we simultaneously train a motion-quality-assessment model that learns the probability that a given skeleton pose sequence is a real or fake human motion. In order to classify the action performed in a video clip, we took two approaches. In the first approach, we train on a sequence of skeleton poses from scratch using random parameters initialization with the same network architecture used in the discriminator of the HP-GAN2 model. For the second approach, we use the discriminator of the HP-GAN2 network, extend it with an action classification branch, and fine tune the end-to-end model on the classification tasks, since the discriminator in HP-GAN2 learned to differentiate between fake and real human motion. So, our hypothesis is that if the discriminator network can differentiate between synthetic and real skeleton poses, then it also has learned some of the dynamics of a real human motion, and that those dynamics are useful in classification as well. We will show through multiple experiments that that is indeed the case. Therefore, our model learns to predict multiple future sequences of human poses from the same input sequence. We also show that the discriminator learns a general representation of human motion by using the learned features in an action recognition task. And we train a motion-quality-assessment network that measure the probability of a given sequence of poses are valid human poses or not. We test our model on two of the largest human pose datasets: NTURGB-D, and Human3.6M. We train on both single and multiple action types. The predictive power of our model for motion estimation is demonstrated by generating multiple plausible futures from the same input and showing the effect of each of the several loss functions in the ablation study. We also show the advantage of switching to GAN from WGAN-GP, which we used in our previous work. Furthermore, we show that it takes less than half the number of epochs to train an activity recognition network by using the features learned from the discriminator

Opinions and attitudes of Cambridge, Mass., residents toward urban renewal.

Thesis (M.S.)--Boston Universit

Media Sensationalism and its Implications on the Public Understanding of Science

Myths, misinformation, and sensationalism. These are common enemies that directly inhibit the public understanding of science. In particular, the media is often responsible for mishandling or otherwise misrepresenting scientific information, historically and presently speaking. Many sources can combat the public understanding of science through pseudoscientific means. This includes but is not limited to religion, the media, politics, or just simple hearsay. For example, Young Earth creationism is deeply rooted in Christian theology, but the beliefs hold no scientific basis. Yet, almost half of Americans still believe in Young Earth creationism. Another such example is anti-vaccination campaigns due to fears of autism-spectrum related disorders. In this case, falsified claims were given illegitimate credibility through the media, and the claims are widely and erroneously contentious to this day. The purpose of this research was to investigate the relationship between an individual\u27s ability to dictate science from pseudoscience and their exposure to sensationalized media. Through means of surveying the university level population, relationships were drawn between how many pseudoscientific beliefs an individual may have versus how they interact with science and the media. The results of the survey showed a general lack of interest or care for science with more pseudoscientific beliefs, yet failed to draw a relationship between pseudoscientific beliefs and a sensationalized media