Effects of pH on Growth of Salvinia molesta Mitchell

Growth of giant salvinia ( Salvinia molesta Mitchell) under different pH regimes was examined at the Lewisville Aquatic Ecosystem Research Facility (LAERF) in Lewisville, Texas.(PDF has 5 pages.

Regeneration of Giant Salvinia from Apical and Axillary Buds following Desiccation or Physical Damage

Can a new giant salvinia infestation occur even if most of the mat is destroyed except for the protected buds? From this study, we are able to conclude that buds can produce new growth under certain stressful conditions. They must be greater than 0.2 cm in length and they must possess greater than 30% moisture content to survive

Cross Pixel Optical Flow Similarity for Self-Supervised Learning

We propose a novel method for learning convolutional neural image representations without manual supervision. We use motion cues in the form of optical flow, to supervise representations of static images. The obvious approach of training a network to predict flow from a single image can be needlessly difficult due to intrinsic ambiguities in this prediction task. We instead propose a much simpler learning goal: embed pixels such that the similarity between their embeddings matches that between their optical flow vectors. At test time, the learned deep network can be used without access to video or flow information and transferred to tasks such as image classification, detection, and segmentation. Our method, which significantly simplifies previous attempts at using motion for self-supervision, achieves state-of-the-art results in self-supervision using motion cues, competitive results for self-supervision in general, and is overall state of the art in self-supervised pretraining for semantic image segmentation, as demonstrated on standard benchmarks

Video Representation Learning by Recognizing Temporal Transformations

We introduce a novel self-supervised learning approach to learn representations of videos that are responsive to changes in the motion dynamics. Our representations can be learned from data without human annotation and provide a substantial boost to the training of neural networks on small labeled data sets for tasks such as action recognition, which require to accurately distinguish the motion of objects. We promote an accurate learning of motion without human annotation by training a neural network to discriminate a video sequence from its temporally transformed versions. To learn to distinguish non-trivial motions, the design of the transformations is based on two principles: 1) To define clusters of motions based on time warps of different magnitude; 2) To ensure that the discrimination is feasible only by observing and analyzing as many image frames as possible. Thus, we introduce the following transformations: forward-backward playback, random frame skipping, and uniform frame skipping. Our experiments show that networks trained with the proposed method yield representations with improved transfer performance for action recognition on UCF101 and HMDB51.Comment: ECCV 202

Extracting inner‐heliosphere solar wind speed information from Heliospheric Imager observations

We present evidence that variability in the STEREO‐A Heliospheric Imager (HI) data is correlated with in situ solar wind speed estimates from WIND, STEREO‐A, and STEREO‐B. For 2008–2012, we compute the variability in HI differenced images in a plane‐of‐sky shell between 20 to 22.5 solar radii and, for a range of position angles, compare daily means of HI variability and in situ solar wind speed estimates. We show that the HI variability data and in situ solar wind speeds have similar temporal autocorrelation functions. Carrington rotation periodicities are well documented for in situ solar wind speeds, but, to our knowledge, this is the first time they have been presented in statistics computed from HI images. In situ solar wind speeds from STEREO‐A, STEREO‐B, and WIND are all are correlated with the HI variability, with a lag that varies in a manner consistent with the longitudinal separation of the in situ monitor and the HI instrument. Unlike many approaches to processing HI observations, our method requires no manual feature tracking; it is automated, is quick to compute, and does not suffer the subjective biases associated with manual classifications. These results suggest we could possibly estimate solar wind speeds in the low heliosphere directly from HI observations. This motivates further investigation, as this could be a significant asset to the space weather forecasting community; it might provide an independent observational constraint on heliospheric solar wind forecasts, through, for example, data assimilation. Finally, these results are another argument for the potential utility of including a HI on an operational space weather mission

A Critical Appraisal and Evaluation of Modern PDFs

We review the present status of the determination of parton distribution functions (PDFs) in the light of the precision requirements for the LHC in Run 2 and other future hadron colliders. We provide brief reviews of all currently available PDF sets and use them to compute cross sections for a number of benchmark processes, including Higgs boson production in gluon-gluon fusion at the LHC. We show that the differences in the predictions obtained with the various PDFs are due to particular theory assumptions made in the fits of those PDFs. We discuss PDF uncertainties in the kinematic region covered by the LHC and on averaging procedures for PDFs, such as advocated by the PDF4LHC15 sets, and provide recommendations for the usage of PDF sets for theory predictions at the LHC.Comment: 70 pages pdflatex, 19 figures, 17 tables; final versio

Exclusive W + photon production in proton-antiproton collisions I: general formalism

We present a detailed computation of the fully exclusive cross section of p + antip --> W + photon + X with X = 0 and 1 jet in the framework of the factorization theorem and dimensional regularization. Order alpha-strong and photon bremsstrahlung contributions are discussed in the MS-bar mass factorization scheme. The resulting expressions are ready to be implemented numerically using Monte Carlo techniques to compute single and double differential cross sections and correlations between outgoing pairs of particles.Comment: ITP-SB-93-72, 40 pages, LateX. 3*4 figures in separate file. ([email protected]) ([email protected]

Interleukin-2 receptor and ovarian cancer.

Interleukin-2 receptor (IL-2R) can be detected in serum. We estimated the IL-2R in the serum of 78 women, of whom 30 were diagnosed as having malignant ovarian tumours, five had non ovarian tumours, one had a negative second look laparotomy, 11 had benign ovarian tumours, three had uterine fibroids and 28 were age-matched controls. The results indicated that the serum IL-2R of these patients was significantly elevated in ovarian cancer patients compared to both controls (P < 0.0001) and benign ovarian tumours (P < 0.0002). There were no significant differences in IL-2R levels between stage of disease and degree of differentiation within the ovarian tumour group