Temporal Cross-Media Retrieval with Soft-Smoothing

Multimedia information have strong temporal correlations that shape the way modalities co-occur over time. In this paper we study the dynamic nature of multimedia and social-media information, where the temporal dimension emerges as a strong source of evidence for learning the temporal correlations across visual and textual modalities. So far, cross-media retrieval models, explored the correlations between different modalities (e.g. text and image) to learn a common subspace, in which semantically similar instances lie in the same neighbourhood. Building on such knowledge, we propose a novel temporal cross-media neural architecture, that departs from standard cross-media methods, by explicitly accounting for the temporal dimension through temporal subspace learning. The model is softly-constrained with temporal and inter-modality constraints that guide the new subspace learning task by favouring temporal correlations between semantically similar and temporally close instances. Experiments on three distinct datasets show that accounting for time turns out to be important for cross-media retrieval. Namely, the proposed method outperforms a set of baselines on the task of temporal cross-media retrieval, demonstrating its effectiveness for performing temporal subspace learning.Comment: To appear in ACM MM 201

Learning Colour Representations of Search Queries

Image search engines rely on appropriately designed ranking features that capture various aspects of the content semantics as well as the historic popularity. In this work, we consider the role of colour in this relevance matching process. Our work is motivated by the observation that a significant fraction of user queries have an inherent colour associated with them. While some queries contain explicit colour mentions (such as 'black car' and 'yellow daisies'), other queries have implicit notions of colour (such as 'sky' and 'grass'). Furthermore, grounding queries in colour is not a mapping to a single colour, but a distribution in colour space. For instance, a search for 'trees' tends to have a bimodal distribution around the colours green and brown. We leverage historical clickthrough data to produce a colour representation for search queries and propose a recurrent neural network architecture to encode unseen queries into colour space. We also show how this embedding can be learnt alongside a cross-modal relevance ranker from impression logs where a subset of the result images were clicked. We demonstrate that the use of a query-image colour distance feature leads to an improvement in the ranker performance as measured by users' preferences of clicked versus skipped images.Comment: Accepted as a full paper at SIGIR 202

Enhancing the operational stability of unencapsulated perovskite solar cells through Cu-Ag bilayer electrode incorporation

We identify a facile strategy that significantly reduces electrode corrosion and device degradation in unencapsulated perovskite solar cells (PSCs) operating in ambient air. By employing Cu-Ag bilayer top electrodes PSCs, we show enhanced operational lifetime compared with devices prepared from single metal (Al, Ag and Cu) analogues. Time-of-flight secondary ion mass spectrometry depth profiles indicate that the insertion of the thin layer of Cu (10nm) below the Ag (100nm) electrode significantly reduces diffusion of species originating in the perovskite active layer into the electron transport layer and electrode. X-ray diffraction (XRD) analysis reveals the mutually beneficial relationship between the bilayer metals, whereby the thermally evaporated Ag inhibits Cu oxidation and the Cu prevents interfacial reactions between the perovskite and Ag. The results here not only demonstrate a simple approach to prevent the electrode and device degradation that enhance lifetime and stability but also give an insight into ageing related ion migration and structural reorganisation

EEG ERP preregistration template

This preregistration template guides researchers who wish to preregister their EEG projects, more specifically studies investigating event-related potentials (ERPs) in the sensor space

A guide for social science journal editors on easing into open science

Journal editors have a large amount of power to advance open science in their respective fields by incentivising and mandating open policies and practices at their journals. The Data PASS Journal Editors Discussion Interface (JEDI, an online community for social science journal editors: www.dpjedi.org) has collated several resources on embedding open science in journal editing (www.dpjedi.org/resources). However, it can be overwhelming as an editor new to open science practices to know where to start. For this reason, we created a guide for journal editors on how to get started with open science. The guide outlines steps that editors can take to implement open policies and practices within their journal, and goes through the what, why, how, and worries of each policy and practice. This manuscript introduces and summarizes the guide (full guide: https://doi.org/10.31219/osf.io/hstcx)

A Domain Adaptation Approach to Improve Speaker Turn Embedding Using Face Representation

This paper proposes a novel approach to improve speaker modeling using knowledge transferred from face representation. In particular, we are interested in learning a discriminative metric which allows speaker turns to be compared directly, which is beneficial for tasks such as diarization and dialogue analysis. Our method improves the embedding space of speaker turns by applying maximum mean discrepancy loss to minimize the disparity between the distributions of facial and acoustic embedded features. This approach aims to discover the shared underlying structure of the two embedded spaces, thus enabling the transfer of knowledge from the richer face representation to the counterpart in speech. Experiments are conducted on broadcast TV news datasets, REPERE and ETAPE, to demonstrate the validity of our method. Quantitative results in verification and clustering tasks show promising improvement, especially in cases where speaker turns are short or the training data size is limited

Multinational characterization of neurological phenotypes in patients hospitalized with COVID-19

Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January–September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7–7.8%, pFDR < 0.001) and unspecified disorders of the brain (8.1%, 5.7–10.5%, pFDR < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19–25%), cerebrovascular diseases (24%, 13–35%), nontraumatic intracranial hemorrhage (34%, 20–50%), encephalitis and/or myelitis (37%, 17–60%) and myopathy (72%, 67–77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease

Difference of clinical features in childhood Mycoplasma pneumoniae pneumonia

<p>Abstract</p> <p>Background</p> <p><it>M. pneumoniae </it>pneumonia (MP) has been reported in 10-40% of community-acquired pneumonia cases. We aimed to evaluate the difference of clinical features in children with MP, according to their age and chest radiographic patterns.</p> <p>Methods</p> <p>The diagnosis of MP was made by examinations at both admission and discharge and by two serologic tests: the indirect microparticle agglutinin assay (≥1:40) and the cold agglutinins titer (≥1:32). A total of 191 children with MP were grouped by age: ≤2 years of age (29 patients), 3-5 years of age (81 patients), and ≥6 years of age (81 patients). They were also grouped by pneumonia pattern: bronchopneumonia group (96 patients) and segmental/lobar pneumonia group (95 patients).</p> <p>Results</p> <p>Eighty-six patients (45%) were seroconverters, and the others showed increased antibody titers during hospitalization. Among the three age groups, the oldest children showed the longest duration of fever, highest C-reactive protein (CRP) values, and the most severe pneumonia pattern. The patients with segmental/lobar pneumonia were older and had longer fever duration and lower white blood cell (WBC) and lymphocyte counts, compared with those with bronchopneumonia. The patient group with the most severe pulmonary lesions had the most prolonged fever, highest CRP, highest rate of seroconverters, and lowest lymphocyte counts. Thrombocytosis was observed in 8% of patients at admission, but in 33% of patients at discharge.</p> <p>Conclusions</p> <p>In MP, older children had more prolonged fever and more severe pulmonary lesions. The severity of pulmonary lesions was associated with the absence of diagnostic IgM antibodies at presentation and lymphocyte count. Short-term paired IgM serologic test may be mandatory for early and definitive diagnosis of MP.</p

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask

Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field