Fast Video Classification via Adaptive Cascading of Deep Models

Recent advances have enabled "oracle" classifiers that can classify across many classes and input distributions with high accuracy without retraining. However, these classifiers are relatively heavyweight, so that applying them to classify video is costly. We show that day-to-day video exhibits highly skewed class distributions over the short term, and that these distributions can be classified by much simpler models. We formulate the problem of detecting the short-term skews online and exploiting models based on it as a new sequential decision making problem dubbed the Online Bandit Problem, and present a new algorithm to solve it. When applied to recognizing faces in TV shows and movies, we realize end-to-end classification speedups of 2.4-7.8x/2.6-11.2x (on GPU/CPU) relative to a state-of-the-art convolutional neural network, at competitive accuracy.Comment: Accepted at IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

Computer simulations of the diffusion of Na+ and Cl− ions across POPC lipid bilayer membranes

We have carried out molecular dynamics simulations using NAMD to study the diffusivity of Na and Cl ions across a POPC lipid bilayer membrane. We show that an imbalance of positively and negatively charged ions on either side of the membrane leads to the diffusion of ions and water molecules. We considered the cases of both weak and very strong charge imbalance across the membrane. The diffusion coefficients of the ions have been determined from the mean square displacements of the particles as a function of time. We find that for strong electrochemical gradients, both the Na and Cl ions diffuse rapidly through pores in the membrane with diffusion coefficients up to ten times larger than in water. Rather surprisingly, we found that although the Na ions are the first to begin the permeation process due to the lower potential barrier that they experience compared to the Cl ions, the latter complete the permeation across the barrier more quickly due to their faster diffusion rates

Molecular dynamics studies of the melting of copper with vacancies and dislocations at high pressures

Molecular dynamics simulations of the melting process of bulk copper were performed using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) with the interatomic potentials being described by the embedded atom method. The aim of the study was to understand the effects of high pressures and defects on the melting temperature. The simulations were visualised using Visual Molecular Dynamics (VMD). The melting temperature of a perfect copper crystal, was found to be slightly higher than the experimentally observed value. The melting temperature as a function of pressure was determined and compared with experiment. Point and line defects, in the form of dislocations, were then introduced into crystal and the new melting temperature of the crystal determined. We find that the melting temperature decreases as the defect density is increased. Additionally, the slope of the melting temperature curve was found to decrease as the pressure was increased while the vacancy formation energy increases with pressure

High net-worth individuals' portfolios : private real estate assets

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2002.Vitae.Includes bibliographical references (leaves 132-136).The asset allocations of private real estate in the investment portfolios of High Net Worth Individuals (HNWIs) indicate that HNWIs' portfolio returns are not at optimum levels on a risk-adjusted basis. More specifically, utilizing Modem Portfolio Theory, existing allocations to private real estate should, arguably be increased by as much as twice its present allocation. This deficiency is due to insufficient conduits and products available at financial institutions for HNWIs. This mismatch has created a supply and demand problem of HNWI demand for and financial institutions' supply of private real estate assets. The current HNWIs allocations were examined using the "Survey of Consumer Finances" (Federal Reserve, 1998). HNWIs capable of private real estate investment were investors whose net worth was $25 million and above. The HNWI allocations and more than twenty years of historical investment returns and volatilities for financial assets and real estate, were the foundation for analyzing the variance between actual and optimum portfolio allocations of private real estate. This comparison highlighted how the entire HNWI segment could double its current real estate allocation to meet the optimal portfolio level. Along with this real estate allocation deficiency, the HNWI segment has grown substantially over the last 10 years. Since this is a growing segment and a potential source of capital for the real estate industry, this thesis specifically identifies the real estate asset allocation inefficiencies, recommends optimum real estate asset allocations, and lists the alternatives and characteristics of investment conduits and products for increased investment in private real estate by HNWIs.by Ramiro Juliá and Rachel Matthai.S.M

Redox behaviour of polyvalent ions in phosphate glass melts and phosphate glasses

Glass melts and glasses with the basie composition NaPO₃ · 2Sr(PO₃)₂ doped with V₂O₅ and Fe₂O₃ were studied with the aid of square-wave voltammetry and spectroscopic methods. From the square-wave voltammetry in the glass melt, a linear dependence of the peak potentials on the temperature was obtained. The Standard enthalpy ΔH⁰, the Standard entropy ΔS⁰ and lg(C_ox/C_red) of the transitions Fe³⁺/Fe²⁺ and V⁴⁺/V³⁺ were calculated. The diffusion coefficients calculated from the peak currents measured follow Arrhenius' law. By means of electron spin resonance, the concentration of V⁴⁺ was determined. The corresponding extinction coefficients for the V⁴⁺ bands were calculated from the absorption spectra. Concerning the vanadium-containing glass, the results of square-wave voltammetry and spectroscopy were compared quantitatively. For the iron-doped glass, the comparison was only qualitative

Unsupervised activity recognition using automatically mined common sense

A fundamental difficulty in recognizing human activities is obtaining the labeled data needed to learn models of those activities. Given emerging sensor technology, however, it is possible to view activity data as a stream of natural language terms. Activity models are then mappings from such terms to activity names, and may be extracted from text corpora such as the web. We show that models so extracted are sufficient to automatically produce labeled segmentations of activity data with an accuracy of 42 % over 26 activities, well above the 3.8 % baseline. The segmentation so obtained is sufficient to bootstrap learning, with accuracy of learned models increasing to 52%. To our knowledge, this is the first human activity inferencing system shown to learn from sensed activity data with no human intervention per activity learned, even for labeling