Create a translational medicine knowledge repository - Research downsizing, mergers and increased outsourcing have reduced the depth of in-house translational medicine expertise and institutional memory at many pharmaceutical and biotech companies: how will they avoid relearning old lessons?

Pharmaceutical industry consolidation and overall research downsizing threatens the ability of companies to benefit from their previous investments in translational research as key leaders with the most knowledge of the successful use of biomarkers and translational pharmacology models are laid off or accept their severance packages. Two recently published books may help to preserve this type of knowledge but much of this type of information is not in the public domain. Here we propose the creation of a translational medicine knowledge repository where companies can submit their translational research data and access similar data from other companies in a precompetitive environment. This searchable repository would become an invaluable resource for translational scientists and drug developers that could speed and reduce the cost of new drug development

Theory of Minds: Understanding Behavior in Groups Through Inverse Planning

Human social behavior is structured by relationships. We form teams, groups, tribes, and alliances at all scales of human life. These structures guide multi-agent cooperation and competition, but when we observe others these underlying relationships are typically unobservable and hence must be inferred. Humans make these inferences intuitively and flexibly, often making rapid generalizations about the latent relationships that underlie behavior from just sparse and noisy observations. Rapid and accurate inferences are important for determining who to cooperate with, who to compete with, and how to cooperate in order to compete. Towards the goal of building machine-learning algorithms with human-like social intelligence, we develop a generative model of multi-agent action understanding based on a novel representation for these latent relationships called Composable Team Hierarchies (CTH). This representation is grounded in the formalism of stochastic games and multi-agent reinforcement learning. We use CTH as a target for Bayesian inference yielding a new algorithm for understanding behavior in groups that can both infer hidden relationships as well as predict future actions for multiple agents interacting together. Our algorithm rapidly recovers an underlying causal model of how agents relate in spatial stochastic games from just a few observations. The patterns of inference made by this algorithm closely correspond with human judgments and the algorithm makes the same rapid generalizations that people do.Comment: published in AAAI 2019; Michael Shum and Max Kleiman-Weiner contributed equall

Coordinate to cooperate or compete:Abstract goals and joint intentions in social interaction

Successfully navigating the social world requires reasoningabout both high-level strategic goals, such as whether to co-operate or compete, as well as the low-level actions neededto achieve those goals. While previous work in experimentalgame theory has examined the former and work on multi-agentsystems has examined the later, there has been little work in-vestigating behavior in environments that require simultaneousplanning and inference across both levels. We develop a hierar-chical model of social agency that infers the intentions of otheragents, strategically decides whether to cooperate or competewith them, and then executes either a cooperative or competi-tive planning program. Learning occurs across both high-levelstrategic decisions and low-level actions leading to the emer-gence of social norms. We test predictions of this model inmulti-agent behavioral experiments using rich video-game likeenvironments. By grounding strategic behavior in a formalmodel of planning, we develop abstract notions of both co-operation and competition and shed light on the computationalnature of joint intentionality

Reinforced Axial Refinement Network for Monocular 3D Object Detection

Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. This is an ill-posed problem with a major difficulty lying in the information loss by depth-agnostic cameras. Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space. To improve the efficiency of sampling, we propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step. This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it. The proposed framework, Reinforced Axial Refinement Network (RAR-Net), serves as a post-processing stage which can be freely integrated into existing monocular 3D detection methods, and improve the performance on the KITTI dataset with small extra computational costs.Comment: Accepted by ECCV 202

On the discrete spectrum of spin-orbit Hamiltonians with singular interactions

We give a variational proof of the existence of infinitely many bound states below the continuous spectrum for spin-orbit Hamiltonians (including the Rashba and Dresselhaus cases) perturbed by measure potentials thus extending the results of J.Bruening, V.Geyler, K.Pankrashkin: J. Phys. A 40 (2007) F113--F117.Comment: 10 pages; to appear in Russian Journal of Mathematical Physics (memorial volume in honor of Vladimir Geyler). Results improved in this versio

Use of Mobile Learning by Resident Physicians in Botswana

With the growth of mobile health in recent years, learning through the use of mobile devices (mobile learning [mLearning]) has gained recognition as a potential method for increasing healthcare providers\u27 access to medical information and resources in resource-limited settings. In partnership with the University of Botswana School of Medicine (SOM), we have been exploring the role of smartphone-based mLearning with resident (physicians in specialty training) education. The SOM, which admitted its first class of medical students and residents in 2009, is committed to providing high-level on-site educational resources for resident physicians, even when practicing in remote locations. Seven residents were trained to use an Android-based myTouch 3G smartphone equipped with data-enabled subscriber identity module (SIM) cards and built-in camera. Phones contained locally loaded point-of-care and drug information applications, a telemedicine application that allows for the submission of cases to local mentors, and e-mail/Web access. Surveys were administered at 4 weeks and 8 weeks following distribution of phones. We found that smartphones loaded with point-of-care tools are effectively utilized by resident physicians in resource-limited settings, both for accessing point-of-care medical information at the bedside and engaging in self-directed learning at home

Asymptotic stability and blow up for a semilinear damped wave equation with dynamic boundary conditions

In this paper we consider a multi-dimensional wave equation with dynamic boundary conditions, related to the Kelvin-Voigt damping. Global existence and asymptotic stability of solutions starting in a stable set are proved. Blow up for solutions of the problem with linear dynamic boundary conditions with initial data in the unstable set is also obtained

Green's functions for parabolic systems of second order in time-varying domains

We construct Green's functions for divergence form, second order parabolic systems in non-smooth time-varying domains whose boundaries are locally represented as graph of functions that are Lipschitz continuous in the spatial variables and 1/2-H\"older continuous in the time variable, under the assumption that weak solutions of the system satisfy an interior H\"older continuity estimate. We also derive global pointwise estimates for Green's function in such time-varying domains under the assumption that weak solutions of the system vanishing on a portion of the boundary satisfy a certain local boundedness estimate and a local H\"older continuity estimate. In particular, our results apply to complex perturbations of a single real equation.Comment: 25 pages, 0 figur

Atomic Resonance and Scattering

Contains research objectives, summary of research and reports on four research projects.Joint Services Electronics Programs (U. S. Army, U. S. Navy, and U. S. Air Force) under Contract DAAB07-71-C-0300National Science Foundation (Grant GP-28679)National Bureau of Standards (Grant NBS2-9011)U. S. Air Force - Office of Scientific Research (Contract F44620-72-C-0057

Experimental modulation of capsule size in Cryptococcus neoformans

Experimental modulation of capsule size is an important technique for the study of the virulence of the encapsulated pathogen Cryptococcus neoformans. In this paper, we summarize the techniques available for experimental modulation of capsule size in this yeast and describe improved methods to induce capsule size changes. The response of the yeast to the various stimuli is highly dependent on the cryptococcal strain. A high CO(2) atmosphere and a low iron concentration have been used classically to increase capsule size. Unfortunately, these stimuli are not reliable for inducing capsular enlargement in all strains. Recently we have identified new and simpler conditions for inducing capsule enlargement that consistently elicited this effect. Specifically, we noted that mammalian serum or diluted Sabouraud broth in MOPS buffer pH 7.3 efficiently induced capsule growth. Media that slowed the growth rate of the yeast correlated with an increase in capsule size. Finally, we summarize the most commonly used media that induce capsule growth in C. neoformans