Statement of David M. Silberman Before the Commission on the Future of Worker-Management Relations

Includes AIEG New Employee Handbook as appendixTestimony_Silberman_081094.pdf: 326 downloads, before Oct. 1, 2020

Effectiveness of community outreach and engagement in recruitment success for a prebirth cohort.

IntroductionWe describe the effectiveness of community outreach and engagement in supporting recruitment for the US National Children's Vanguard Study between 2009 and 2012.MethodsThirty-seven study locations used 1 of 4 strategies to recruit 18-49-year-old pregnant or trying to conceive women: (1) Initial Vanguard Study used household-based recruitment; (2) Direct Outreach emphasized self-referral; (3) Enhanced Household-Based Recruitment enhanced Initial Vanguard Study strategies; and (4) Provider-Based Recruitment recruited through healthcare providers. Outreach and engagement included advance letters, interactions with healthcare providers, participation in community events, contacts with community organizations, and media outreach.ResultsAfter 1-2 years, 41%-74% of 9844 study-eligible women had heard about the National Children's Vanguard Study when first approached. Women who heard were 1.5-3 times more likely to consent. Hearing via word-of-mouth or the media most frequently predicted consent. The more sources women heard from the higher the odds of consent.ConclusionsWe conclude that tailored outreach and engagement facilitate recruitment in cohort studies

Assessing the Quality of Actions

While recent advances in computer vision have provided reliable methods to recognize actions in both images and videos, the problem of assessing how well people perform actions has been largely unexplored in computer vision. Since methods for assessing action quality have many real-world applications in healthcare, sports, and video retrieval, we believe the computer vision community should begin to tackle this challenging problem. To spur progress, we introduce a learning-based framework that takes steps towards assessing how well people perform actions in videos. Our approach works by training a regression model from spatiotemporal pose features to scores obtained from expert judges. Moreover, our approach can provide interpretable feedback on how people can improve their action. We evaluate our method on a new Olympic sports dataset, and our experiments suggest our framework is able to rank the athletes more accurately than a non-expert human. While promising, our method is still a long way to rivaling the performance of expert judges, indicating that there is significant opportunity in computer vision research to improve on this difficult yet important task.National Science Foundation (U.S.). Graduate Research FellowshipGoogle (Firm) (Research Award)United States. Office of Naval Research. Multidisciplinary University Research Initiative (N000141010933

Learning Shape Priors for Single-View 3D Completion and Reconstruction

The problem of single-view 3D shape completion or reconstruction is challenging, because among the many possible shapes that explain an observation, most are implausible and do not correspond to natural objects. Recent research in the field has tackled this problem by exploiting the expressiveness of deep convolutional networks. In fact, there is another level of ambiguity that is often overlooked: among plausible shapes, there are still multiple shapes that fit the 2D image equally well; i.e., the ground truth shape is non-deterministic given a single-view input. Existing fully supervised approaches fail to address this issue, and often produce blurry mean shapes with smooth surfaces but no fine details. In this paper, we propose ShapeHD, pushing the limit of single-view shape completion and reconstruction by integrating deep generative models with adversarially learned shape priors. The learned priors serve as a regularizer, penalizing the model only if its output is unrealistic, not if it deviates from the ground truth. Our design thus overcomes both levels of ambiguity aforementioned. Experiments demonstrate that ShapeHD outperforms state of the art by a large margin in both shape completion and shape reconstruction on multiple real datasets.Comment: ECCV 2018. The first two authors contributed equally to this work. Project page: http://shapehd.csail.mit.edu

Purification, growth, and characterization of Zn(x)Cd(1-x)Se crystals

The purification of starting materials which were used in the growth of Zn(x)Cd(1-x)Se (x = 0.2) single crystals using the traveling solution method (TSM) is reported. Up to 13 cm long single crystals and as grown resistivities of 6 x 10(exp 12) ohm/cm could be achieved. Infrared and Raman spectra of Zn(0.2)Cd(0.8)Se are also presented and discussed

Cosmological Density and Power Spectrum from Peculiar Velocities: Nonlinear Corrections and PCA

We allow for nonlinear effects in the likelihood analysis of galaxy peculiar velocities, and obtain ~35%-lower values for the cosmological density parameter Om and the amplitude of mass-density fluctuations. The power spectrum in the linear regime is assumed to be a flat LCDM model (h=0.65, n=1, COBE) with only Om as a free parameter. Since the likelihood is driven by the nonlinear regime, we "break" the power spectrum at k_b=0.2 h/Mpc and fit a power law at k>k_b. This allows for independent matching of the nonlinear behavior and an unbiased fit in the linear regime. The analysis assumes Gaussian fluctuations and errors, and a linear relation between velocity and density. Tests using proper mock catalogs demonstrate a reduced bias and a better fit. We find for the Mark3 and SFI data Om_m=0.32+-0.06 and 0.37+-0.09 respectively, with sigma_8*Om^0.6 = 0.49+-0.06 and 0.63+-0.08, in agreement with constraints from other data. The quoted 90% errors include cosmic variance. The improvement in likelihood due to the nonlinear correction is very significant for Mark3 and moderately so for SFI. When allowing deviations from LCDM, we find an indication for a wiggle in the power spectrum: an excess near k=0.05 and a deficiency at k=0.1 (cold flow). This may be related to the wiggle seen in the power spectrum from redshift surveys and the second peak in the CMB anisotropy. A chi^2 test applied to modes of a Principal Component Analysis (PCA) shows that the nonlinear procedure improves the goodness of fit and reduces a spatial gradient of concern in the linear analysis. The PCA allows addressing spatial features of the data and fine-tuning the theoretical and error models. It shows that the models used are appropriate for the cosmological parameter estimation performed. We address the potential for optimal data compression using PCA.Comment: 18 pages, LaTex, uses emulateapj.sty, ApJ in press (August 10, 2001), improvements to text and figures, updated reference

Accurate and linear time pose estimation from points and lines

The final publication is available at link.springer.comThe Perspective-n-Point (PnP) problem seeks to estimate the pose of a calibrated camera from n 3Dto-2D point correspondences. There are situations, though, where PnP solutions are prone to fail because feature point correspondences cannot be reliably estimated (e.g. scenes with repetitive patterns or with low texture). In such scenarios, one can still exploit alternative geometric entities, such as lines, yielding the so-called Perspective-n-Line (PnL) algorithms. Unfortunately, existing PnL solutions are not as accurate and efficient as their point-based counterparts. In this paper we propose a novel approach to introduce 3D-to-2D line correspondences into a PnP formulation, allowing to simultaneously process points and lines. For this purpose we introduce an algebraic line error that can be formulated as linear constraints on the line endpoints, even when these are not directly observable. These constraints can then be naturally integrated within the linear formulations of two state-of-the-art point-based algorithms, the OPnP and the EPnP, allowing them to indistinctly handle points, lines, or a combination of them. Exhaustive experiments show that the proposed formulation brings remarkable boost in performance compared to only point or only line based solutions, with a negligible computational overhead compared to the original OPnP and EPnP.Peer ReviewedPostprint (author's final draft

Arctic shipping emissions inventories and future scenarios

This paper presents 5 km×5 km Arctic emissions inventories of important greenhouse gases, black carbon and other pollutants under existing and future (2050) scenarios that account for growth of shipping in the region, potential diversion traffic through emerging routes, and possible emissions control measures. These high-resolution, geospatial emissions inventories for shipping can be used to evaluate Arctic climate sensitivity to black carbon (a short-lived climate forcing pollutant especially effective in accelerating the melting of ice and snow), aerosols, and gaseous emissions including carbon dioxide. We quantify ship emissions scenarios which are expected to increase as declining sea ice coverage due to climate change allows for increased shipping activity in the Arctic. A first-order calculation of global warming potential due to 2030 emissions in the high-growth scenario suggests that short-lived forcing of ~4.5 gigagrams of black carbon from Arctic shipping may increase global warming potential due to Arctic ships' CO<sub>2</sub> emissions (~42 000 gigagrams) by some 17% to 78%. The paper also presents maximum feasible reduction scenarios for black carbon in particular. These emissions reduction scenarios will enable scientists and policymakers to evaluate the efficacy and benefits of technological controls for black carbon, and other pollutants from ships

SIRT6 Is Required for Normal Retinal Function

The retina is one of the major energy consuming tissues within the body. In this context, synaptic transmission between light-excited rod and cone photoreceptors and downstream ON-bipolar neurons is a highly demanding energy consuming process. Sirtuin 6 (SIRT6), a NAD-dependent deacylase, plays a key role in regulating glucose metabolism. In this study, we demonstrate that SIRT6 is highly expressed in the retina, controlling levels of histone H3K9 and H3K56 acetylation. Notably, despite apparent normal histology, SIRT6 deficiency caused major retinal transmission defects concomitant to changes in expression of glycolytic genes and glutamate receptors, as well as elevated levels of apoptosis in inner retina cells. Our results identify SIRT6 as a critical modulator of retinal function, likely through its effects on chromatin

Some open questions in "wave chaos"

The subject area referred to as "wave chaos", "quantum chaos" or "quantum chaology" has been investigated mostly by the theoretical physics community in the last 30 years. The questions it raises have more recently also attracted the attention of mathematicians and mathematical physicists, due to connections with number theory, graph theory, Riemannian, hyperbolic or complex geometry, classical dynamical systems, probability etc. After giving a rough account on "what is quantum chaos?", I intend to list some pending questions, some of them having been raised a long time ago, some others more recent