Cosmic Rays and Climate

Among the most puzzling questions in climate change is that of solar-climate variability, which has attracted the attention of scientists for more than two centuries. Until recently, even the existence of solar-climate variability has been controversial - perhaps because the observations had largely involved temporary correlations between climate and the sunspot cycle. Over the last few years, however, diverse reconstructions of past climate change have revealed clear associations with cosmic ray variations recorded in cosmogenic isotope archives, providing persuasive evidence for solar or cosmic ray forcing of the climate. However, despite the increasing evidence of its importance, solar climate variability is likely to remain controversial until a physical mechanism is established. Although this remains a mystery, observations suggest that cloud cover may be influenced by cosmic rays, which are modulated by the solar wind and, on longer time scales, by the geomagnetic field and by the galactic environment of Earth. Two different classes of microphysical mechanisms have been proposed to connect cosmic rays with clouds: firstly, an influence of cosmic rays on the production of cloud condensation nuclei and, secondly, an influence of cosmic rays on the global electrical circuit in the atmosphere and, in turn, on ice nucleation and other cloud microphysical processes. Considerable progress on understanding ion-aerosol-cloud processes has been made in recent years, and the results are suggestive of a physically- plausible link between cosmic rays, clouds and climate. However, a concerted effort is now required to carry out definitive laboratory measurements of the fundamental physical and chemical processes involved, and to evaluate their climatic significance with dedicated field observations and modelling studies.Comment: 42 pages, 19 figure

New approaches for automated data processing of annually laminated sediments

International audienceLaminated sediments, like evaporites and biogenic lake sediments, provide high-resolution paleo-climate records. Yet detection and counting of laminae causes still problems because sedimentary structures are often disturbed. In the past laminated rocks often were analysed manually - a tedious and subjective work. The present study describes four automated approaches for lamina detection based on 1d grey-scale vectors. Best results are obtained with a newly developed algorithm, called Adaptive Template Method (ATM) in combination with the Hilbert transform. ATM improves the signal to noise ratio of the grey-value signal. Its basic idea is to extract first a characteristic waveform, the template, which describes the typical grey-value variation transverse to the laminae. This is a kind of "template learning" process, which in practice is done by an appropriate averaging method. This template is in a second step used for processing the whole sample. One calculates the overlap of the template with the actual signal, the grey-value variation along the core, as function of position in core direction. This method generates a new signal with maxima at positions, where the template optimally matches the original signal. The new time-series is called AT-transform. It is smoother than the initial data sequence. High frequency noise and local trend effects are suppressed. Afterwards, the AT-transform can be analysed with the Hilbert transformation for extracting phase information. The data processing methods are tested both on artificial data sequences and on a seasonally laminated sedimentary record of the Oligocene Baruth Maar (Germany). Although ATM is no panacea for highly disturbed signals, our comparison with other approaches shows that it provides the best results. The combination of ATM and the Hilbert transform allows to detect clearly long-term oscillations in the sedimentation patterns and thus cycles in climatic variations

The Importance of the Instantaneous Phase in Detecting Faces with Convolutional Neural Networks

Convolutional Neural Networks (CNN) have provided new and accurate methods for processing digital images and videos. Yet, training CNNs is extremely demanding in terms of computational resources. Also, for simple applications, the standard use of transfer learning also tends to require far more resources than what may be needed. Furthermore, the final systems tend to operate as black boxes that are difficult to interpret. The current thesis considers the problem of detecting faces from the AOLME video dataset. The AOLME dataset consists of a large video collection of group interactions that are recorded in unconstrained classroom environments. For the thesis, still image frames were extracted at every minute from 18 24-minute videos. Then, each video frame was divided into 9x5 blocks with 50x50 pixels each. For each of the 19440 blocks, the percentage of face pixels was set as ground truth. Face detection was then defined as a regression problem for determining the face pixel percentage for each block. For testing different methods, 12 videos were used for training and validation. The remaining 6 videos were used for testing. The thesis examines the impact of using the instantaneous phase for the AOLME block-based face detection application. For comparison, the thesis compares the use of the Frequency modulation image based on the instantaneous phase, the use of the instantaneous amplitude, and the original gray scale image. To generate the FM and AM inputs, the thesis uses dominant component analysis that aims to decrease the training overhead while maintaining interpretability. The results indicate that the use of the FM image yielded about the same performance as the MobileNet V2 architecture (AUC of 0.78 vs 0.79), with vastly reduced training times. Training was 7x faster for an Intel Xeon with a GTX 1080 based desktop and 11x faster on a laptop with Intel i5 with a GTX 1050. Furthermore, the proposed architecture trains 123x less parameters than what is needed for MobileNet V2. The FM-based neural network architecture uses a single convolutional layer. In comparison, the full LeNet-5 on the same image block using the original image could not be trained for face detection (AUC of 0.5)

The Importance of the Instantaneous Phase in Detecting Faces with Convolutional Neural Networks

Convolutional Neural Networks (CNN) have provided new and accurate methods for processing digital images and videos. Yet, training CNNs is extremely demanding in terms of computational resources. Also, for specific applications, the standard use of transfer learning also tends to require far more resources than what may be needed. Furthermore, the final systems tend to operate as black boxes that are difficult to interpret. The current thesis considers the problem of detecting faces from the AOLME video dataset. The AOLME dataset consists of a large video collection of group interactions that are recorded in unconstrained classroom environments. For the thesis, still image frames were extracted at every minute from 18 24-minute videos. Then, each video frame was divided into 9x5 blocks with 50x50 pixels each. For each of the 19440 blocks, the percentage of face pixels was set as ground truth. Face detection was then defined as a regression problem for determining the face pixel percentage for each block. For testing different methods, 12 videos were used for training and validation. The remaining 6 videos were used for testing. The thesis examines the impact of using the instantaneous phase for the AOLME block-based face detection application. For comparison, the thesis compares the use of the Frequency Modulation image based on the instantaneous phase, the use of the instantaneous amplitude, and the original gray scale image. To generate the FM and AM inputs, the thesis uses dominant component analysis that aims to decrease the training overhead while maintaining interpretability.Comment: Master Thesi

Centrifugal instability of Stokes layers in crossflow: the case of a forced cylinder wake

The wake flow around a circular cylinder at $Re\approx100$ performing rotatory oscillations has been thoroughly discussed in the literature, mostly focusing on the modifications to the natural B\'enard-von K\'arm\'an vortex street that result from the forced shedding modes locked to the rotatory oscillation frequency. The usual experimental and theoretical frameworks at these Reynolds numbers are quasi-two-dimensional, since the secondary instabilities bringing a three-dimensional structure to the cylinder wake flow occur only at higher Reynolds numbers. In the present paper we show that a three-dimensional structure can appear below the usual three-dimensionalization threshold, when forcing with frequencies lower than the natural vortex shedding frequency, at high amplitudes, as a result of a previously unreported mechanism: a pulsed centrifugal instability of the oscillating Stokes layer at the wall of the cylinder. The present numerical investigation lets us in this way propose a physical explanation for the turbulence-like features reported in the recent experimental study of D'Adamo et al. (2011).Comment: 18 pages, 13 figures. To appear in Proc. Roy. Soc. A. For supplementary video material, see http://vimeo.com/12315202

Human Attention Detection Using AM-FM Representations

Human activity detection from digital videos presents many challenges to the computer vision and image processing communities. Recently, many methods have been developed to detect human activities with varying degree of success. Yet, the general human activity detection problem remains very challenging, especially when the methods need to work “in the wild” (e.g., without having precise control over the imaging geometry). The thesis explores phase-based solutions for (i) detecting faces, (ii) back of the heads, (iii) joint detection of faces and back of the heads, and (iv) whether the head is looking to the left or the right, using standard video cameras without any control on the imaging geometry. The proposed phase-based approach is based on the development of simple and robust methods that relie on the use of Amplitude Modulation - Frequency Modulation (AM-FM) models. The approach is validated using video frames extracted from the Advancing Outof- school Learning in Mathematics and Engineering (AOLME) project. The dataset consisted of 13,265 images from ten students looking at the camera, and 6,122 images from five students looking away from the camera. For the students facing the camera, the method was able to correctly classify 97.1% of them looking to the left and 95.9% of them looking to the right. For the students facing the back of the camera, the method was able to correctly classify 87.6% of them looking to the left and 93.3% of them looking to the right. The results indicate that AM-FM based methods hold great promise for analyzing human activity videos