The Fiber Walk: A Model of Tip-Driven Growth with Lateral Expansion

Tip-driven growth processes underlie the development of many plants. To date, tip-driven growth processes have been modelled as an elongating path or series of segments without taking into account lateral expansion during elongation. Instead, models of growth often introduce an explicit thickness by expanding the area around the completed elongated path. Modelling expansion in this way can lead to contradictions in the physical plausibility of the resulting surface and to uncertainty about how the object reached certain regions of space. Here, we introduce "fiber walks" as a self-avoiding random walk model for tip-driven growth processes that includes lateral expansion. In 2D, the fiber walk takes place on a square lattice and the space occupied by the fiber is modelled as a lateral contraction of the lattice. This contraction influences the possible follow-up steps of the fiber walk. The boundary of the area consumed by the contraction is derived as the dual of the lattice faces adjacent to the fiber. We show that fiber walks generate fibers that have well-defined curvatures, enable the identification of the process underlying the occupancy of physical space. Hence, fiber walks provide a base from which to model both the extension and expansion of physical biological objects with finite thickness.Comment: Plos One (in press

The Impact of Galactic Winds on the Angular Momentum of Disk Galaxies in the Illustris Simulation

Observed galactic disks have specific angular momenta similar to expectations for typical dark matter halos in $\Lambda$CDM. Cosmological hydrodynamical simulations have recently reproduced this similarity in large galaxy samples by including strong galactic winds, but the exact mechanism that achieves this is not yet clear. Here we present an analysis of key aspects contributing to this relation: angular momentum selection and evolution of Lagrangian mass elements as they accrete onto dark matter halos, condense into Milky Way-scale galaxies, and join the $z=0$ stellar phase. We contrast this evolution in the Illustris simulation with that in a simulation without galactic winds, where the $z=0$ angular momentum is $\approx0.6$ dex lower. We find that winds induce differences between these simulations in several ways: increasing angular momentum, preventing angular momentum loss, and causing $z=0$ stars to sample the accretion-time angular momentum distribution of baryons in a biased way. In both simulations, gas loses on average $\approx0.4$ dex between accreting onto halos and first accreting onto central galaxies. In Illustris, this is followed by $\approx0.2$ dex gains in the `galactic wind fountain' and no further net evolution past the final accretion onto the galaxy. Without feedback, further losses of $\approx0.2$ dex occur in the gas phase inside the galaxies. An additional $\approx0.15$ dex difference arises from feedback preferentially selecting higher angular momentum gas at accretion by expelling gas that is poorly aligned. These and additional effects of similar magnitude are discussed, suggesting a complex origin of the similarity between the specific angular momenta of galactic disks and typical halos.Comment: Accepted to ApJ. 13 pages, 10 figures. Key figures are 1, 2, and

Generic Tubelet Proposals for Action Localization

We develop a novel framework for action localization in videos. We propose the Tube Proposal Network (TPN), which can generate generic, class-independent, video-level tubelet proposals in videos. The generated tubelet proposals can be utilized in various video analysis tasks, including recognizing and localizing actions in videos. In particular, we integrate these generic tubelet proposals into a unified temporal deep network for action classification. Compared with other methods, our generic tubelet proposal method is accurate, general, and is fully differentiable under a smoothL1 loss function. We demonstrate the performance of our algorithm on the standard UCF-Sports, J-HMDB21, and UCF-101 datasets. Our class-independent TPN outperforms other tubelet generation methods, and our unified temporal deep network achieves state-of-the-art localization results on all three datasets

Atomic resolved material displacement on graphite surfaces by scanning tunnelling microscopy

Atomic scale modifications and subsequent atomic resolution imaging has been achieved on the highly oriented pyrolytic graphite surface in air. Application of short pulse voltages, above a minimum threshold voltage of 3.5 V, across the tunneling gap results in the displacement of a layer or more of atoms to form a hole and create a neighboring mound or ‘‘nanodot’’ from the displaced atoms. We have found a correlation between the hole and ‘‘nanodot’’ volume at the atomic level and observe an asymmetric displacement of material in all cases of feature creation. Nanofeatures as small as four carbon atoms at beta sites have been created. Our experimental observations are consistent with the modification process depending on the gradient in the electric field induced by the rise time of the bias pulse voltage and not the pulse duration. Interesting faceting behavior has also been observed around some hole edges. Tip bias pulsing sometimes induced a tip, and not a surface modification, resulting in a change in the observed tunneling image

The feedback-related negativity reflects the binary evaluation of good versus bad outcomes

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Towards dense object tracking in a 2D honeybee hive

From human crowds to cells in tissue, the detection and efficient tracking of multiple objects in dense configurations is an important and unsolved problem. In the past, limitations of image analysis have restricted studies of dense groups to tracking a single or subset of marked individuals, or to coarse-grained group-level dynamics, all of which yield incomplete information. Here, we combine convolutional neural networks (CNNs) with the model environment of a honeybee hive to automatically recognize all individuals in a dense group from raw image data. We create new, adapted individual labeling and use the segmentation architecture U-Net with a loss function dependent on both object identity and orientation. We additionally exploit temporal regularities of the video recording in a recurrent manner and achieve near human-level performance while reducing the network size by 94% compared to the original U-Net architecture. Given our novel application of CNNs, we generate extensive problem-specific image data in which labeled examples are produced through a custom interface with Amazon Mechanical Turk. This dataset contains over 375,000 labeled bee instances across 720 video frames at 2 FPS, representing an extensive resource for the development and testing of tracking methods. We correctly detect 96% of individuals with a location error of ~7% of a typical body dimension, and orientation error of 12 degrees, approximating the variability of human raters. Our results provide an important step towards efficient image-based dense object tracking by allowing for the accurate determination of object location and orientation across time-series image data efficiently within one network architecture.Comment: 15 pages, including supplementary figures. 1 supplemental movie available as an ancillary fil

Stalking in Alaska

Originally published in the Alaska Justice Forum 24(1): 1, 7–12 (Spring 2007).This study examined 267 cases with a stalking charge reported to Alaska State Troopers from 1994 to 2005, and excluded any cases reported to local or municipal departments. We also examined the legal resolutions for cases that were reported from 1999-2004. * Over 50% of reports occurred in B detachment (Southcentral Alaska) and D detachment (Interior Alaska). Three units (Fairbanks AST Enforcement, Palmer AST Enforcement, and Soldotna AST Enforcement) handled 49% of reports. Thirty-five percent of the charges were for stalking in the first degree and 65% were for stalking in the second degree. * Most suspects (91%) were male and most victims (89%) were female. Most suspects (78%) were White and most victims (86%) were also White. On average, suspects were 36 years old while victims were 33 years old. Twenty percent of suspects had used alcohol, but only 2% of victims had used alcohol. Fifty-four percent of suspects were, or had been, in a romantic relationship with the victim. An additional 35% of suspects were friends or acquaintances of the victim. * The most common forms of stalking included standing outside or visiting the victim's home (in 54% of charges), making unsolicited phone calls to victims (in 51% of charges), following the victim (in 39% of charges), threatening to physically assault the victim (in 36% of charges), harassing the victim's family and friends (in 28% of charges), trying to communicate with the victim in other ways (in 27% of charges), standing outside or visiting the victim's work (in 20% of charges), physically assaulting the victim (in 19% of charges), sending the victim unsolicited mail (in 15% of charges), and vandalizing the victim's home (in 13% of charges). Forty-five percent of behaviors occurred primarily at the victim's home, while 27% occurred primarily in cyberspace. * Seventy-five percent of the cases reported between 1999-2004 were referred for prosecution, 55% were accepted for prosecution, and 40% resulted in a conviction on at least one charge. Cases with suspects who violated protective orders were 20% more likely to be referred for prosecution, were 19% more likely to be accepted, and were 41% more likely to result in a conviction

It's worse than you thought : the feedback negativity and violations of reward prediction in gambling tasks

The reinforcement learning theory suggests that the feedback negativity should be larger when feedback is unexpected. Two recent studies found, however, that the feedback negativity was unaffected by outcome probability. To further examine this issue, participants in the present studies made reward predictions on each trial of a gambling task where objective reward probability was indicated by a cue. In Study 1, participants made reward predictions following the cue, but prior to their gambling choice; in Study 2, predictions were made following their gambling choice. Predicted and unpredicted outcomes were associated with equivalent feedback negativities in Study 1. In Study 2, however, the feedback negativity was larger for unpredicted outcomes. These data suggest that the magnitude of the feedback negativity is sensitive to violations of reward prediction, but that this effect may depend on the close coupling of prediction and outcome