Learning to Extract Motion from Videos in Convolutional Neural Networks

This paper shows how to extract dense optical flow from videos with a convolutional neural network (CNN). The proposed model constitutes a potential building block for deeper architectures to allow using motion without resorting to an external algorithm, \eg for recognition in videos. We derive our network architecture from signal processing principles to provide desired invariances to image contrast, phase and texture. We constrain weights within the network to enforce strict rotation invariance and substantially reduce the number of parameters to learn. We demonstrate end-to-end training on only 8 sequences of the Middlebury dataset, orders of magnitude less than competing CNN-based motion estimation methods, and obtain comparable performance to classical methods on the Middlebury benchmark. Importantly, our method outputs a distributed representation of motion that allows representing multiple, transparent motions, and dynamic textures. Our contributions on network design and rotation invariance offer insights nonspecific to motion estimation

Conductance statistics from a large array of sub-10 nm molecular junctions

Devices made of few molecules constitute the miniaturization limit that both inorganic and organic-based electronics aspire to reach. However, integration of millions of molecular junctions with less than 100 molecules each has been a long technological challenge requiring well controlled nanometric electrodes. Here we report molecular junctions fabricated on a large array of sub-10 nm single crystal Au nanodots electrodes, a new approach that allows us to measure the conductance of up to a million of junctions in a single conducting Atomic Force Microscope (C-AFM) image. We observe two peaks of conductance for alkylthiol molecules. Tunneling decay constant (beta) for alkanethiols, is in the same range as previous studies. Energy position of molecular orbitals, obtained by transient voltage spectroscopy, varies from peak to peak, in correlation with conductance values.Comment: ACS Nano (in press

An objective comparison of cell-tracking algorithms

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge

Ultrafast Relaxation of Photoexcited Carriers: The Role of Coherence in the Generation Process

A self-consistent description of the ultrafast dynamics of photoexcited carriers in semiconductors based on a generalized Monte Carlo solution of the semiconductor Bloch equations is presented. The problem of photogeneration and its theoretical description are discussed. We show that some of the approaches commonly used fail in describing correctly the effect of carrier-carrier interaction in the low-density limit. By including terms which have the structure of ‘‘in-scattering'' terms (vertex corrections) for the interband polarization, the experimentally observed features in the carrier dynamics are well described in the whole density range

Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity

Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan