Disability

People with disabilities (PWD) are the fastest growing minority social group in the world. Moreover, this group is one in which many, if not all individuals, will eventually join due to accidents, injuries, illnesses, wear and tear on aging bodies, and genetic factors. Disabilities can be physical, cognitive, social, and/or emotional. The disability community overlaps with people of all races, ethnicities, age groups, genders, sexual orientations/ expressions, and socioeconomic statuses, although PWD are overrepresented among people who are economically disadvantaged and under-served in health care, environmental safety, nutrition, and other basic needs. While the proportion of people with disabilities increases with age, the majority of people with disabilities remains under the age of 65

Spire stimulates nucleation by Cappuccino and binds both ends of actin filaments.

The actin nucleators Spire and Cappuccino synergize to promote actin assembly, but the mechanism of their synergy is controversial. Together these proteins promote the formation of actin meshes, which are conserved structures that regulate the establishment of oocyte polarity. Direct interaction between Spire and Cappuccino is required for oogenesis and for in vitro synergistic actin assembly. This synergy is proposed to be driven by elongation and the formation of a ternary complex at filament barbed ends, or by nucleation and interaction at filament pointed ends. To mimic the geometry of Spire and Cappuccino in vivo, we immobilized Spire on beads and added Cappuccino and actin. Barbed ends, protected by Cappuccino, grow away from the beads while pointed ends are retained, as expected for nucleation-driven synergy. We found that Spire is sufficient to bind barbed ends and retain pointed ends of actin filaments near beads and we identified Spire's barbed-end binding domain. Loss of barbed-end binding increases nucleation by Spire and synergy with Cappuccino in bulk pyrene assays and on beads. Importantly, genetic rescue by the loss-of-function mutant indicates that barbed-end binding is not necessary for oogenesis. Thus, increased nucleation is a critical element of synergy both in vitro and in vivo

The Merging History of Massive Black Holes

We investigate a hierarchical structure formation scenario describing the evolution of a Super Massive Black Holes (SMBHs) population. The seeds of the local SMBHs are assumed to be 'pregalactic' black holes, remnants of the first POPIII stars. As these pregalactic holes become incorporated through a series of mergers into larger and larger halos, they sink to the center owing to dynamical friction, accrete a fraction of the gas in the merger remnant to become supermassive, form a binary system, and eventually coalesce. A simple model in which the damage done to a stellar cusps by decaying BH pairs is cumulative is able to reproduce the observed scaling relation between galaxy luminosity and core size. An accretion model connecting quasar activity with major mergers and the observed BH mass-velocity dispersion correlation reproduces remarkably well the observed luminosity function of optically-selected quasars in the redshift range 1<z<5. We finally asses the potential observability of the gravitational wave background generated by the cosmic evolution of SMBH binaries by the planned space-born interferometer LISA.Comment: 4 pages, 2 figures, Contribute to "Multiwavelength Cosmology", Mykonos, Greece, June 17-20, 200

How to Host a Data Competition: Statistical Advice for Design and Analysis of a Data Competition

Data competitions rely on real-time leaderboards to rank competitor entries and stimulate algorithm improvement. While such competitions have become quite popular and prevalent, particularly in supervised learning formats, their implementations by the host are highly variable. Without careful planning, a supervised learning competition is vulnerable to overfitting, where the winning solutions are so closely tuned to the particular set of provided data that they cannot generalize to the underlying problem of interest to the host. This paper outlines some important considerations for strategically designing relevant and informative data sets to maximize the learning outcome from hosting a competition based on our experience. It also describes a post-competition analysis that enables robust and efficient assessment of the strengths and weaknesses of solutions from different competitors, as well as greater understanding of the regions of the input space that are well-solved. The post-competition analysis, which complements the leaderboard, uses exploratory data analysis and generalized linear models (GLMs). The GLMs not only expand the range of results we can explore, they also provide more detailed analysis of individual sub-questions including similarities and differences between algorithms across different types of scenarios, universally easy or hard regions of the input space, and different learning objectives. When coupled with a strategically planned data generation approach, the methods provide richer and more informative summaries to enhance the interpretation of results beyond just the rankings on the leaderboard. The methods are illustrated with a recently completed competition to evaluate algorithms capable of detecting, identifying, and locating radioactive materials in an urban environment.Comment: 36 page

Inducing safer oblique trees without costs

Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than in the other class. Several authors have tackled this problem by developing cost-sensitive decision tree learning algorithms or have suggested ways of changing the distribution of training examples to bias the decision tree learning process so as to take account of costs. A prerequisite for applying such algorithms is the availability of costs of misclassification. Although this may be possible for some applications, obtaining reasonable estimates of costs of misclassification is not easy in the area of safety. This paper presents a new algorithm for applications where the cost of misclassifications cannot be quantified, although the cost of misclassification in one class is known to be significantly higher than in another class. The algorithm utilizes linear discriminant analysis to identify oblique relationships between continuous attributes and then carries out an appropriate modification to ensure that the resulting tree errs on the side of safety. The algorithm is evaluated with respect to one of the best known cost-sensitive algorithms (ICET), a well-known oblique decision tree algorithm (OC1) and an algorithm that utilizes robust linear programming

Photonic microwave generation with high-power photodiodes

We utilize and characterize high-power, high-linearity modified uni-traveling carrier (MUTC) photodiodes for low-phase-noise photonic microwave generation based on optical frequency division. When illuminated with picosecond pulses from a repetition-rate-multiplied gigahertz Ti:sapphire modelocked laser, the photodiodes can achieve 10 GHz signal power of +14 dBm. Using these diodes, a 10 GHz microwave tone is generated with less than 500 attoseconds absolute integrated timing jitter (1 Hz-10 MHz) and a phase noise floor of -177 dBc/Hz. We also characterize the electrical response, amplitude-to-phase conversion, saturation and residual noise of the MUTC photodiodes.Comment: 3 pages, 3 figure

The impact of bidding aggregation levels on truckload rates

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2010.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 79-80).The objective of this thesis was to determine if line-haul rates are impacted by bid type, and if aggregation of bidding lanes can reduce costs for both shippers and carriers. Using regression analysis, we developed a model to isolate and test the cost effects that influence line-haul rate for long-haul shipments. We have determined that aggregation of low-volume lanes from point-to-point lanes to aggregated lanes can provide costs savings when lanes with origins and destinations in close proximity to each other can be bundled. In addition, bidding out region-to-region lanes can supplement point-to-point lanes by reducing the need to turn to the spot market. The model shows that bundling lanes can provide significant cost savings to a shipper because contract lanes of any type are on average less costly than spot moves. This thesis provides guidelines and suggestions for aggregation when creating bids during the first stage of the truckload procurement process.by Julia M. Collins and R. Ryan Quinlan.M.Eng.in Logistic

Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees

We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that these star/galaxy classifications are expected to be reliable for approximately 22 million objects with r < ~20. The general machine learning environment Data-to-Knowledge and supercomputing resources enabled extensive investigation of the decision tree parameter space. This work presents the first public release of objects classified in this way for an entire SDSS data release. The objects are classified as either galaxy, star or nsng (neither star nor galaxy), with an associated probability for each class. To demonstrate how to effectively make use of these classifications, we perform several important tests. First, we detail selection criteria within the probability space defined by the three classes to extract samples of stars and galaxies to a given completeness and efficiency. Second, we investigate the efficacy of the classifications and the effect of extrapolating from the spectroscopic regime by performing blind tests on objects in the SDSS, 2dF Galaxy Redshift and 2dF QSO Redshift (2QZ) surveys. Given the photometric limits of our spectroscopic training data, we effectively begin to extrapolate past our star-galaxy training set at r ~ 18. By comparing the number counts of our training sample with the classified sources, however, we find that our efficiencies appear to remain robust to r ~ 20. As a result, we expect our classifications to be accurate for 900,000 galaxies and 6.7 million stars, and remain robust via extrapolation for a total of 8.0 million galaxies and 13.9 million stars. [Abridged]Comment: 27 pages, 12 figures, to be published in ApJ, uses emulateapj.cl