Quantifying the impact of emission outbursts and non-stationary flow on eddy covariance CH₄ flux measurements using wavelet techniques

Methane flux measurements by the eddy-covariance technique are subject to large uncertainties, particularly linked to the partly highly intermittent nature of methane emissions. Outbursts of high methane emissions, termed event fluxes, hold the potential to introduce systematic biases into derived methane budgets, since under such conditions the assumption of stationarity of the flow is violated. In this study, we investigate the net impact of this effect by comparing eddy-covariance fluxes against a wavelet-derived reference that is not negatively influenced by non-stationarity. Our results demonstrate that methane emission events influenced 3–4 % of the flux measurements, and did not lead to systematic biases in methane budgets for the analyzed summer season; however, the presence of events substantially increased uncertainties in short-term flux rates. The wavelet results provided an excellent reference to evaluate the performance of three different gapfilling approaches for eddy-covariance methane fluxes, and we show that none of them could reproduce the range of observed flux rates. The integrated performance of the gapfilling methods for the longer-term dataset varied between the two eddy-covariance towers involved in this study, and we show that gapfilling remains a large source of uncertainty linked to limited insights into the mechanisms governing the short-term variability in methane emissions. With the capability to broaden our observational methane flux database to a wider range of conditions, including the direct resolution of short term variability at the order of minutes, wavelet-derived fluxes hold the potential to generate new insight into methane exchange processes with the atmosphere, and therefore also improve our understanding of the underlying processes

SNX27-Mediated Recycling of Neuroligin-2 Regulates Inhibitory Signaling

GABAA receptors mediate fast inhibitory transmission in the brain, and their number can be rapidly up- or downregulated to alter synaptic strength. Neuroligin-2 plays a critical role in the stabilization of synaptic GABAA receptors and the development and maintenance of inhibitory synapses. To date, little is known about how the amount of neuroligin-2 at the synapse is regulated and whether neuroligin-2 trafficking affects inhibitory signaling. Here, we show that neuroligin-2, when internalized to endosomes, co-localizes with SNX27, a brain-enriched cargo-adaptor protein that facilitates membrane protein recycling. Direct interaction between the PDZ domain of SNX27 and PDZ-binding motif in neuroligin-2 enables membrane retrieval of neuroligin-2, thus enhancing synaptic neuroligin-2 clusters. Furthermore, SNX27 knockdown has the opposite effect. SNX27-mediated up- and downregulation of neuroligin-2 surface levels affects inhibitory synapse composition and signaling strength. Taken together, we show a role for SNX27-mediated recycling of neuroligin-2 in maintenance and signaling of the GABAergic synapse

Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowing to aggregate the predictions of the classifiers trained by each learner. The proposed method aggregates the base classifiers using a probabilistic model relying on Gaussian copulas. Experiments on logistic regressor ensembles demonstrate competing accuracy and increased robustness in case of dependent classifiers. A companion python implementation can be downloaded at https://github.com/john-klein/DELC

Robust face recognition by an albedo based 3D morphable model

Large pose and illumination variations are very challenging for face recognition. The 3D Morphable Model (3DMM) approach is one of the effective methods for pose and illumination invariant face recognition. However, it is very difficult for the 3DMM to recover the illumination of the 2D input image because the ratio of the albedo and illumination contributions in a pixel intensity is ambiguous. Unlike the traditional idea of separating the albedo and illumination contributions using a 3DMM, we propose a novel Albedo Based 3D Morphable Model (AB3DMM), which removes the illumination component from the images using illumination normalisation in a preprocessing step. A comparative study of different illumination normalisation methods for this step is conducted on PIE and Multi-PIE databases. The results show that overall performance of our method outperforms state-of-the-art methods

The biker-glove pattern of congenital melanocytic nevi.

Congenital melanocytic nevi (CMN) are common birthmarks with 20% occurring on the limbs. We describe 4 patients with acral CMN with a "biker-glove" distribution with sparing of the distal digits, as has previously been described in acral infantile hemangiomas (IH). The existence of the biker-glove pattern suggests that CMN arise from early mutations in melanocyte precursors and supports the recently described Kinsler-Larue hypothesis of mesenchymal distribution of melanocyte migration occurring in a circular field from a central point. Developmental errors in mesenchymal precursors with similar migration patterns may explain this shared pattern among CMN and IH

A novel Markov logic rule induction strategy for characterizing sports video footage

The grounding of high-level semantic concepts is a key requirement of video annotation systems. Rule induction can thus constitute an invaluable intermediate step in characterizing protocol-governed domains, such as broadcast sports footage. We here set out a novel “clause grammar template” approach to the problem of rule-induction in video footage of court games that employs a second-order meta grammar for Markov Logic Network construction. The aim is to build an adaptive system for sports video annotation capable, in principle, both of learning ab initio and also adaptively transferring learning between distinct rule domains. The method is tested with respect to both a simulated game predicate generator and also real data derived from tennis footage via computer-vision based approaches including HOG3D based player-action classification, Hough-transform based court detection, and graph-theoretic ball-tracking. Experiments demonstrate that the method exhibits both error resilience and learning transfer in the court domain context. Moreover the clause template approach naturally generalizes to any suitably-constrained, protocol-governed video domain characterized by feature noise or detector error

Automatic annotation of tennis games: an integration of audio, vision, and learning

Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level

Domain anomaly detection in machine perception: a system architecture and taxonomy

We address the problem of anomaly detection in machine perception. The concept of domain anomaly is introduced as distinct from the conventional notion of anomaly used in the literature. We propose a unified framework for anomaly detection which exposes the multifacetted nature of anomalies and suggest effective mechanisms for identifying and distinguishing each facet as instruments for domain anomaly detection. The framework draws on the Bayesian probabilistic reasoning apparatus which clearly defines concepts such as outlier, noise, distribution drift, novelty detection (object, object primitive), rare events, and unexpected events. Based on these concepts we provide a taxonomy of domain anomaly events. One of the mechanisms helping to pinpoint the nature of anomaly is based on detecting incongruence between contextual and noncontextual sensor(y) data interpretation. The proposed methodology has wide applicability. It underpins in a unified way the anomaly detection applications found in the literature

Media life

Review of the book 'Media Life' by Mark Deuze

Phosphorylation of neuroligin-2 by PKA regulates its cell surface abundance and synaptic stabilization

The trans-synaptic adhesion molecule neuroligin-2 (NL2) is essential for the development and function of inhibitory synapses. NL2 recruits the postsynaptic scaffold protein gephyrin, which, in turn, stabilizes γ-aminobutyric acid type A receptors (GABAARs) in the postsynaptic domain. Thus, the amount of NL2 at the synapse can control synaptic GABAAR concentration to tune inhibitory neurotransmission efficacy. Here, using biochemistry, imaging, single-particle tracking, and electrophysiology, we uncovered a key role for cAMP-dependent protein kinase (PKA) in the synaptic stabilization of NL2. We found that PKA-mediated phosphorylation of NL2 at Ser714 caused its dispersal from the synapse and reduced NL2 surface amounts, leading to a loss of synaptic GABAARs. Conversely, enhancing the stability of NL2 at synapses by abolishing PKA-mediated phosphorylation led to increased inhibitory signaling. Thus, PKA plays a key role in regulating NL2 function and GABA-mediated synaptic inhibition