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

Does Every Quasar Harbor A Blazar?

Assuming there is a blazar type continuum in every radio-loud quasar, we find that the free-free heating due to the beamed infrared continuum can greatly enhance collisionally excited lines, and thus explain the stronger CIV $\lambda$1549 line emission observed in radio loud quasars. We further predict that the CIV line should show variability {\it not} associated with observed continuum or Ly$\alpha$ variability.Comment: 15 pages, 3 figures; to appear in Astrophys. J. Let

Illuminating dark matter and primordial black holes with interstellar antiprotons

Interstellar antiproton fluxes can arise from dark matter annihilating or decaying into quarks or gluons that subsequently fragment into antiprotons. Evaporation of primordial black holes also can produce a significant antiproton cosmic-ray flux. Since the background of secondary antiprotons from spallation has an interstellar energy spectrum that peaks at \sim 2\gev and falls rapidly for energies below this, low-energy measurements of cosmic antiprotons are useful in the search for exotic antiproton sources. However, measurement of the flux near the earth is challenged by significant uncertainties from the effects of the solar wind. We suggest evading this problem and more effectively probing dark-matter signals by placing an antiproton spectrometer aboard an interstellar probe currently under discussion. We address the experimental challenges of a light, low-power-consuming detector, and present an initial design of such an instrument. This experimental effort could significantly increase our ability to detect, and have confidence in, a signal of exotic, nonstandard antiproton sources. Furthermore, solar modulation effects in the heliosphere would be better quantified and understood by comparing results to inverse modulated data derived from existing balloon and space-based detectors near the earth.Comment: 18 pages, 3 figure

Sympathetic autonomic dysfunction and impaired cardiovascular performance in higher risk surgical patients: implications for perioperative sympatholysis

OBJECTIVE: Recent perioperative trials have highlighted the urgent need for a better understanding of why sympatholytic drugs intended to reduce myocardial injury are paradoxically associated with harm (stroke, myocardial infarction). We hypothesised that following a standardised autonomic challenge, a subset of patients may demonstrate excessive sympathetic activation which is associated with exercise-induced ischaemia and impaired cardiac output. METHODS: Heart rate rise during unloaded pedalling (zero workload) prior to the onset of cardiopulmonary exercise testing (CPET) was measured in 2 observation cohorts of elective surgical patients. The primary outcome was exercise-evoked, ECG-defined ischaemia (>1 mm depression; lead II) associated with an exaggerated increase in heart rate (EHRR ≥12 bpm based on prognostic data for all-cause cardiac death in preceding epidemiological studies). Secondary outcomes included cardiopulmonary performance (oxygen pulse (surrogate for left ventricular stroke volume), peak oxygen consumption (VO2peak), anaerobic threshold (AT)) and perioperative heart rate. RESULTS: EHRR was present in 40.4-42.7% in both centres (n=232, n=586 patients). Patients with EHRR had higher heart rates perioperatively (p<0.05). Significant ST segment depression during CPET was more common in EHRR patients (relative risk 1.7 (95% CI 1.3 to 2.1); p<0.001). EHRR was associated with 11% (95%CI 7% to 15%) lower predicted oxygen pulse (p<0.0001), consistent with impaired left ventricular function. CONCLUSIONS: EHRR is common and associated with ECG-defined ischaemia and impaired cardiac performance. Perioperative sympatholysis may further detrimentally affect cardiac output in patients with this phenotype

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 2FPS, 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°, 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.Funding for this work was provided by the OIST Graduate University to ASM and GS. Additional funding was provided by KAKENHI grants 16H06209 and 16KK0175 from the Japan Society for the Promotion of Science to AS

On the linear fractional self-attracting diffusion

In this paper, we introduce the linear fractional self-attracting diffusion driven by a fractional Brownian motion with Hurst index 1/2<H<1, which is analogous to the linear self-attracting diffusion. For 1-dimensional process we study its convergence and the corresponding weighted local time. For 2-dimensional process, as a related problem, we show that the renormalized self-intersection local time exists in L^2 if $\frac12<H<\frac3{4}$.Comment: 14 Pages. To appear in Journal of Theoretical Probabilit

The influence of phosphonic acid protonation state on the efficiency of bis(diimine)copper(I) dye-sensitized solar cells

We present an investigation of the effects of a change in the protonation state of the phosphonic acid anchoring ligand in the dye [Cu(H 4 1 )( 2 )][PF 6 ] (H 4 1 = ((6,6'-dimethyl-[2,2'-bipyridine]-4,4'-diyl)bis(4,1-phenylene))bis(phosphonic acid), 2 = 4,4'-bis(4-bromophenyl)-6,6'-dimethyl-2,2'-bipyridine) on the performance of n-type dye-sensitized solar cells (DSCs). FTO/TiO 2 electrodes were immersed in solutions of H 4 1 in the presence of base (0–4 equivalents). TiO 2 -anchored heteroleptic copper(I) sensitizers were subsequently formed by ligand exchange between the homoleptic complex [Cu( 2 ) 2 ][PF 6 ] and the anchored ligand [H 4– n 1 ] n – . The results demonstrate that the addition of one equivalent of base during the initial surface functionalization can afford up to a 26% increase in DSC efficiency, while the addition of ≥3 equivalents of base significantly hinders DSC performance. Deprotonation of H 4 1 has been investigated using 1 H and 31 P NMR spectroscopic titrations. Further insight into DSC performance has been gained by using electrochemical impedance spectroscopy, and a comparison is made between DSCs in which the working electrodes are either pre-treated with a base, or exposed to a base post heteroleptic copper(I) dye-assembly

Distinct Fractions of an Artemisia scoparia Extract Contain Compounds With Novel Adipogenic Bioactivity

Adipocytes are important players in metabolic health and disease, and disruption of adipocyte development or function contributes to metabolic dysregulation. Hence, adipocytes are significant targets for therapeutic intervention in obesity and metabolic syndrome. Plants have long been sources for bioactive compounds and drugs. In previous studies, we screened botanical extracts for effects on adipogenesis in vitro and discovered that an ethanolic extract of Artemisia scoparia (SCO) could promote adipocyte differentiation. To follow up on these studies, we have used various separation methods to identify the compound(s) responsible for SCO's adipogenic properties. Fractions and subfractions of SCO were tested for effects on lipid accumulation and adipogenic gene expression in differentiating 3T3-L1 adipocytes. Fractions were also analyzed by Ultra Performance Liquid Chromatography- Mass Spectrometry (UPLC-MS), and resulting peaks were putatively identified through high resolution, high mass accuracy mass spectrometry, literature data, and available natural products databases. The inactive fractions contained mostly quercetin derivatives and chlorogenates, including chlorogenic acid and 3,5-dicaffeoylquinic acid, which had no effects on adipogenesis when tested individually, thus ruling them out as pro-adipogenic bioactives in SCO. Based on these studies we have putatively identified the principal constituents in SCO fractions and subfractions that promoted adipocyte development and fat cell gene expression as prenylated coumaric acids, coumarin monoterpene ethers, 6-demethoxycapillarisin and two polymethoxyflavones

Unraveling critical dynamics: The formation and evolution of topological textures

We study the formation of topological textures in a nonequilibrium phase transition of an overdamped classical O(3) model in 2+1 dimensions. The phase transition is triggered through an external, time-dependent effective mass, parameterized by quench timescale \tau. When measured near the end of the transition the texture separation and the texture width scale respectively as \tau^(0.39 \pm 0.02) and \tau^(0.46 \pm 0.04), significantly larger than \tau^(0.25) predicted from the Kibble-Zurek mechanism. We show that Kibble-Zurek scaling is recovered at very early times but that by the end of the transition the power-laws result instead from a competition between the length scale determined at freeze-out and the ordering dynamics of a textured system. In the context of phase ordering these results suggest that the multiple length scales characteristic of the late-time ordering of a textured system derive from the critical dynamics of a single nonequilibrium correlation length. In the context of defect formation these results imply that significant evolution of the defect network can occur before the end of the phase transition. Therefore a quantitative understanding of the defect network at the end of the phase transition generally requires an understanding of both critical dynamics and the interactions among topological defects.Comment: 12 pages, revtex, 9 figures in eps forma