Population mapping in informal settlements with high-resolution satellite imagery and equitable ground-truth

We propose a generalizable framework for the population estimation of dense, informal settlements in low-income urban areas–so called ’slums’–using high-resolution satellite imagery. Precise population estimates are a crucial factor for efficient resource allocations by government authorities and NGO’s, for instance in medical emergencies. We utilize equitable ground-truth data, which is gathered in collaboration with local communities: Through training and community mapping, the local population contributes their unique domain knowledge, while also maintaining agency over their data. This practice allows us to avoid carrying forward potential biases into the modeling pipeline, which might arise from a less rigorous ground-truthing approach. We contextualize our approach in respect to the ongoing discussion within the machine learning community, aiming to make real-world machine learning applications more inclusive, fair and accountable. Because of the resource intensive ground-truth generation process, our training data is limited. We propose a gridded population estimation model, enabling flexible and customizable spatial resolutions. We test our pipeline on three experimental site in Nigeria, utilizing pre-trained and fine-tune vision networks to overcome data sparsity. Our findings highlight the difficulties of transferring common benchmark models to real-world tasks. We discuss this and propose steps forward

Community structures, interactions and dynamics in London’s bicycle sharing network

Bikesharing schemes are transportation systems that not only provide an efficient mode of transportation in congested urban areas, but also improve last-mile connectivity with public transportation and local accessibility. Bikesharing schemes around the globe generate detailed trip data sets with spatial and temporal dimensions, which, with proper mining and analysis, reveal valuable information on urban mobility patterns. In this paper, we study the London bicycle sharing dataset to explore community structures. Using a novel clustering technique, we derive distinctive behavioural patterns and assess community interactions and spatio-temporal dynamics. The analyses reveal self-contained, interconnected and hybrid clusters that mimic London’s physical structure. Exploring changes over time, we find geographically isolated and specialized communities to be relatively consistent, while the remaining system exhibits volatility, especially during and around peak commuting times. By increasing our understanding of the collective behaviour of the bikesharing users, this analysis supports policy appraisal, operational decision-making and motivates improvements in infrastructure design and management

Probing the A1 to L10 Transformation in FeCuPt Using the First Order Reversal Curve Method

The A1- L10 phase transformation has been investigated in (001) FeCuPt thin films prepared by atomic-scale multilayer sputtering and rapid thermal annealing (RTA). Traditional x-ray diffraction is not always applicable in generating a true order parameter, due to non-ideal crystallinity of the A1 phase. Using the first-order reversal curve (FORC) method, the A1 and L10 phases are deconvoluted into two distinct features in the FORC distribution, whose relative intensities change with the RTA temperature. The L10 ordering takes place via a nucleation-and-growth mode. A magnetization-based phase fraction is extracted, providing a quantitative measure of the L10 phase homogeneity.Comment: 17 pages, 5 figures, 4 page supplementary material (4 figures

Disability, ICT and eLearning Platforms: Faculty-Facing Embedded Work Tools in Learning Management Systems

This paper contributes to the current discussion in the field of human-computer interaction design (HCI) on the accessibility and design of eLearning tools embedded in the online platforms for higher education. Presenting the preliminary results of a longitudinal study of the accessibility of the faculty-facing pages of Canvas learning management system, it aims at drawing the attention of designers, developers, and manufacturers to the barriers erected by the ableist LMS designs for disabled faculty. The paper asks for improvements in design processes by embracing participatory design methods and by paying attention to the recommendations included in this paper

Ready ... Go: Amplitude of the fMRI Signal Encodes Expectation of Cue Arrival Time

What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the “go” signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a “countdown” condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in “no-go” conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals

Interaction proteomics of synapse protein complexes

The brain integrates complex types of information, and executes a wide range of physiological and behavioral processes. Trillions of tiny organelles, the synapses, are central to neuronal communication and information processing in the brain. Synaptic transmission involves an intricate network of synaptic proteins that forms the molecular machinery underlying transmitter release, activation, and modulation of transmitter receptors and signal transduction cascades. These processes are dynamically regulated and underlie neuroplasticity, crucial to learning and memory formation. In recent years, interaction proteomics has increasingly been used to elucidate the constituents of synaptic protein complexes. Unlike classic hypothesis-based assays, interaction proteomics detects both known and novel interactors without bias. In this trend article, we focus on the technical aspects of recent proteomics to identify synapse protein complexes, and the complementary methods used to verify the protein–protein interaction. Moreover, we discuss the experimental feasibility of performing global analysis of the synapse protein interactome

Diffuse Alveolar Hemorrhage

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Diffuse alveolar hemorrhage[DAH] is a serious condition that can be life threatening. It can be caused by a constellation of disorders which presents with hemoptysis, anemia, and diffuse alveolar infiltrates. Respiratory failure from DAH can be so severe that it has been called an ARDS mimic/imitator. Early recognition is crucial because prompt diagnosis and treatment are required for survival. DAH should be distinguished from other causes of pulmonary hemorrhage caused by localized pulmonary abnormalities and the bronchial circulation. Early bronchoscopy with bronchoalveolar lavage (BAL) is generally required to confirm the diagnosis of DAH and rule out infection. Progressively bloody bronchoalveolar lavage samples can distinguish DAH. Systemic vasculitis is one of the most common causes of DAH and can be pathologically defined by the presence of cellular inflammation, vessel destruction, tissue necrosis, and eventually, organ dysfunction. Corticosteroids and immunosuppressive agents remain the gold standard for the treatment. The following case illustrates a patient who was dependent on dialysis, then presented with hemoptysis. Bronchoscopy demonstrated progressively bloody bronchoalveolar lavage samples consistent with diffuse alveolar hemorrhage. Serologic testing was consistent with microscopic polyangiitis. The patient experienced a clinical remission with cyclophosphamide and corticosteroids