Collective Production and Incentives

We analyse incentive problems in collective production environments where contributors are compensated according to their observed and ranked efforts. This provides incentives to the contributors to choose first best efforts

Constraints on Primordial Magnetic Fields from Planck combined with the South Pole Telescope CMB B-mode polarization measurements

A primordial magnetic field (PMF) present before recombination can leave specific signatures on the cosmic microwave background (CMB) fluctuations. Of particular importance is its contribution to the B-mode polarization power spectrum. Indeed, vortical modes sourced by the PMF can dominate the B-mode power spectrum on small scales, as they survive damping up to a small fraction of the Silk length. Therefore, measurements of the B-mode polarization at high-$\ell$ , such as the one recently performed by the South Pole Telescope (SPT), have the potential to provide stringent constraints on the PMF. We use the publicly released SPT B-mode polarization spectrum, along with the temperature and polarization data from the Planck satellite, to derive constraints on the magnitude, the spectral index and the energy scale at which the PMF was generated. We find that, while Planck data constrains the magnetic amplitude to $B_{1 \, \text{Mpc}} < 3.3$ nG at 95\% confidence level (CL), the SPT measurement improves the constraint to $B_{1 \, \text{Mpc}} < 1.5$ nG. The magnetic spectral index, $n_B$, and the time of the generation of the PMF are unconstrained. For a nearly scale-invariant PMF, predicted by simplest inflationary magnetogenesis models, the bound from Planck+SPT is $B_{1 \, \text{Mpc}} < 1.2$ nG at 95% CL. For PMF with $n_B=2$, expected for fields generated in post-inflationary phase transitions, the 95% CL bound is $B_{1 \, \text{Mpc}} < 0.002$ nG, corresponding to the magnetic fraction of the radiation density $\Omega_{B\gamma} < 10^{-3}$ or the effective field $B_{\rm eff} < 100$ nG. The patches for the Boltzmann code CAMB and the Markov Chain Monte Carlo engine CosmoMC, incorporating the PMF effects on CMB, are made publicly available.Comment: 12 pages, 9 figures, 4 table

Mutated TP53 is a marker of increased VEGF expression: analysis of 7,525 pan-cancer tissues.

Anti-angiogenic therapies are an important class of anti-cancer treatment drugs. However, their efficacy is limited to certain tumors and would benefit from identifying a biomarker predictive of therapeutic response. TP53 (tumor protein p53) is a tumor suppressor gene frequently mutated in cancer and implicated in cell-cycle regulation, apoptosis, and angiogenesis. Data from 7,525 unique tumor samples (representing 30 tumor cohorts) were retrieved from the TCGA database to analyze the relationship between TP53-mutation status and VEGFA (vascular endothelial growth factor A) expression. Univariate analyses were done using a Mann-Whitney univariate test or Fisher's exact test. Parameters with a p-value (p)≤0.1 in univariate analysis were selected for follow-up multivariate analyses, including TP53-mutation status, cancer cohorts, cancer subtypes, and VEGFA expression. Our analysis demonstrates statistically significant increases in VEGFA mRNA tissue expression in TP53-mutated adenocarcinomas (but not in squamous cancers) compared to TP53 wild-type tumors. This association holds true in multivariate analyses and remains independent of HIF-1α and MDM2 overexpression. Our findings provide additional evidence that TP53 mutations are linked to the VEGF pathway, potentially offering insight into the mechanism behind increased sensitivity to anti-angiogenic therapies observed in some TP53-mutant tumors

High-speed in vitro intensity diffraction tomography

We demonstrate a label-free, scan-free intensity diffraction tomography technique utilizing annular illumination (aIDT) to rapidly characterize large-volume three-dimensional (3-D) refractive index distributions in vitro. By optimally matching the illumination geometry to the microscope pupil, our technique reduces the data requirement by 60 times to achieve high-speed 10-Hz volume rates. Using eight intensity images, we recover volumes of ∼350 μm  ×  100 μm  ×  20  μm, with near diffraction-limited lateral resolution of   ∼  487  nm and axial resolution of   ∼  3.4  μm. The attained large volume rate and high-resolution enable 3-D quantitative phase imaging of complex living biological samples across multiple length scales. We demonstrate aIDT’s capabilities on unicellular diatom microalgae, epithelial buccal cell clusters with native bacteria, and live Caenorhabditis elegans specimens. Within these samples, we recover macroscale cellular structures, subcellular organelles, and dynamic micro-organism tissues with minimal motion artifacts. Quantifying such features has significant utility in oncology, immunology, and cellular pathophysiology, where these morphological features are evaluated for changes in the presence of disease, parasites, and new drug treatments. Finally, we simulate the aIDT system to highlight the accuracy and sensitivity of the proposed technique. aIDT shows promise as a powerful high-speed, label-free computational microscopy approach for applications where natural imaging is required to evaluate environmental effects on a sample in real time.https://arxiv.org/abs/1904.06004Accepted manuscrip

Collective Production and Incentives

We analyse incentive problems in collective production environments where contributors are compensated according to their observed and ranked efforts. This provides incentives to the contributors to choose first best efforts.

Learning from Logged Implicit Exploration Data

We provide a sound and consistent foundation for the use of \emph{nonrandom} exploration data in "contextual bandit" or "partially labeled" settings where only the value of a chosen action is learned. The primary challenge in a variety of settings is that the exploration policy, in which "offline" data is logged, is not explicitly known. Prior solutions here require either control of the actions during the learning process, recorded random exploration, or actions chosen obliviously in a repeated manner. The techniques reported here lift these restrictions, allowing the learning of a policy for choosing actions given features from historical data where no randomization occurred or was logged. We empirically verify our solution on two reasonably sized sets of real-world data obtained from Yahoo!