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.

A search for solar neutrons on a long duration balloon flight

The EOSCOR 3 detector, designed to measure the flux of solar neutrons, was flown on a long duration RACOON balloon flight from Australia during Jan. through Feb, 1983. The Circum-global flight lasted 22 days. No major solar activity occurred during the flight and thus only an upper limit to the solar flare neutrons flux is given. The atmospheric neutron response is compared with that obtained on earlier flights from Palestine, Texas

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

Accurate prediction of H₃O⁺ and D₃O⁺ sensitivity coefficients to probe a variable proton-to-electron mass ratio

The mass sensitivity of the vibration–rotation–inversion transitions of H316O+, H318O+, and D316O+ is investigated variationally using the nuclear motion program TROVE (Yurchenko, Thiel & Jensen). The calculations utilize new high-level ab initio potential energy and dipole moment surfaces. Along with the mass dependence, frequency data and Einstein A coefficients are computed for all transitions probed. Particular attention is paid to the Δ|k| = 3 and Δ|k − l| = 3 transitions comprising the accidentally coinciding |J, K = 0, v2 = 0+〉 and |J, K = 3, v2 = 0−〉 rotation–inversion energy levels. The newly computed probes exhibit sensitivities comparable to their ammonia and methanol counterparts, thus demonstrating their potential for testing the cosmological stability of the proton-to-electron mass ratio. The theoretical TROVE results are in close agreement with sensitivities obtained using the non-rigid and rigid inverter approximate models, confirming that the ab initio theory used in the present study is adequate

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

Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound

In medical imaging, manual annotations can be expensive to acquire and sometimes infeasible to access, making conventional deep learning-based models difficult to scale. As a result, it would be beneficial if useful representations could be derived from raw data without the need for manual annotations. In this paper, we propose to address the problem of self-supervised representation learning with multi-modal ultrasound video-speech raw data. For this case, we assume that there is a high correlation between the ultrasound video and the corresponding narrative speech audio of the sonographer. In order to learn meaningful representations, the model needs to identify such correlation and at the same time understand the underlying anatomical features. We designed a framework to model the correspondence between video and audio without any kind of human annotations. Within this framework, we introduce cross-modal contrastive learning and an affinity-aware self-paced learning scheme to enhance correlation modelling. Experimental evaluations on multi-modal fetal ultrasound video and audio show that the proposed approach is able to learn strong representations and transfers well to downstream tasks of standard plane detection and eye-gaze prediction.Comment: MICCAI 2020 (early acceptance