The Impact of Gamification Design on the Success of Health and Fitness Apps

Gamification has been increasingly employed in health-related apps in recent years. However, the effect of gamification design on the success of health and fitness apps remains unknown and has not been investigated before. This study attempts to identify what gamification elements are frequently used in the design of health and fitness apps and to empirically quantify their effects on app downloads and user ratings of these apps. We construct a rich dataset that includes information about the daily downloads, ratings and gamification design elements of 2,462 health and fitness apps on the Apple App Store. Our sample contains 924 paid apps and 1,538 free apps. This study contributes to both the gamification and mobile app literatures and provides important implications for app developers who intend to adopt gamification in mobile app design

IDEA: Interactive DoublE Attentions from Label Embedding for Text Classification

Current text classification methods typically encode the text merely into embedding before a naive or complicated classifier, which ignores the suggestive information contained in the label text. As a matter of fact, humans classify documents primarily based on the semantic meaning of the subcategories. We propose a novel model structure via siamese BERT and interactive double attentions named IDEA ( Interactive DoublE Attentions) to capture the information exchange of text and label names. Interactive double attentions enable the model to exploit the inter-class and intra-class information from coarse to fine, which involves distinguishing among all labels and matching the semantical subclasses of ground truth labels. Our proposed method outperforms the state-of-the-art methods using label texts significantly with more stable results.Comment: Accepted by ICTAI202

CELLCOUNTER: Novel Open-Source Software for Counting Cell Migration and Invasion In Vitro

Transwell Boyden chamber based migration/invasion assay is a simple and extensively used approach for the characterization of cell motility in vitro. Cell motility is quantified by counting the number of cells that pass through the filter membrane. The counting is usually performed manually, which is laborious and error prone. We have therefore developed CELLCOUNTER, an application that is capable of recognizing and counting the total number of cells through an intuitive graphical user interface. The counting can be performed in batch, and the counting results can be visualized and further curated manually. CELLCOUNTER will be helpful in streamlining the experimental process and improving the reliability of the data acquisition

ADBench: Anomaly Detection Benchmark

Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data? In this work, we answer these key questions by conducting (to our best knowledge) the most comprehensive anomaly detection benchmark with 30 algorithms on 57 benchmark datasets, named ADBench. Our extensive experiments (98,436 in total) identify meaningful insights into the role of supervision and anomaly types, and unlock future directions for researchers in algorithm selection and design. With ADBench, researchers can easily conduct comprehensive and fair evaluations for newly proposed methods on the datasets (including our contributed ones from natural language and computer vision domains) against the existing baselines. To foster accessibility and reproducibility, we fully open-source ADBench and the corresponding results.Comment: NeurIPS 2022. All authors contribute equally and are listed alphabetically. Code available at https://github.com/Minqi824/ADBenc

Fast Association Tests for Genes with FAST

Gene-based tests of association can increase the power of a genome-wide association study by aggregating multiple independent effects across a gene or locus into a single stronger signal. Recent gene-based tests have distinct approaches to selecting which variants to aggregate within a locus, modeling the effects of linkage disequilibrium, representing fractional allele counts from imputation, and managing permutation tests for p-values. Implementing these tests in a single, efficient framework has great practical value. Fast ASsociation Tests (Fast) addresses this need by implementing leading gene-based association tests together with conventional SNP-based univariate tests and providing a consolidated, easily interpreted report. Fast scales readily to genome-wide SNP data with millions of SNPs and tens of thousands of individuals, provides implementations that are orders of magnitude faster than original literature reports, and provides a unified framework for performing several gene based association tests concurrently and efficiently on the same data. Availability: https://bitbucket.org/baderlab/fast/downloads/FAST.tar.gz, with documentation at https://bitbucket.org/baderlab/fast/wiki/Hom

Where Have All the Interactions Gone? Estimating the Coverage of Two-Hybrid Protein Interaction Maps

Yeast two-hybrid screens are an important method for mapping pairwise physical interactions between proteins. The fraction of interactions detected in independent screens can be very small, and an outstanding challenge is to determine the reason for the low overlap. Low overlap can arise from either a high false-discovery rate (interaction sets have low overlap because each set is contaminated by a large number of stochastic false-positive interactions) or a high false-negative rate (interaction sets have low overlap because each misses many true interactions). We extend capture–recapture theory to provide the first unified model for false-positive and false-negative rates for two-hybrid screens. Analysis of yeast, worm, and fly data indicates that 25% to 45% of the reported interactions are likely false positives. Membrane proteins have higher false-discovery rates on average, and signal transduction proteins have lower rates. The overall false-negative rate ranges from 75% for worm to 90% for fly, which arises from a roughly 50% false-negative rate due to statistical undersampling and a 55% to 85% false-negative rate due to proteins that appear to be systematically lost from the assays. Finally, statistical model selection conclusively rejects the Erdös-Rényi network model in favor of the power law model for yeast and the truncated power law for worm and fly degree distributions. Much as genome sequencing coverage estimates were essential for planning the human genome sequencing project, the coverage estimates developed here will be valuable for guiding future proteomic screens. All software and datasets are available in Datasets S1 and S2, Figures S1–S5, and Tables S1−S6, and are also available from our Web site, http://www.baderzone.org

(E)-2,3-Bis(4-methoxy­phen­yl)acrylic acid

In the title mol­ecule, C17H16O4, the angle between the aromatic ring planes is 69.1 (6)°. The crystal structure is stabilized by inter­molecular O—H⋯O hydrogen bonds; mol­ecules related by a centre of symmetry are linked to form inversion dimers

Green finance, renewable energy investment, and environmental protection: empirical evidence from B.R.I.C.S. countries

Environmental degradation has become a severe concern for the globe; therefore, policymakers in emerging economies are trying to meet the environmental standards. Nowadays, economies have shifted their energy pattern from non-renewable to renewable energy (R.E.U.), but its cost is too high. Undoubtedly, the financial sector also performs well in facilitating such green activities. Therefore, the current study investigates the role of R.E.U. and green finance in environmental quality and collects the data for B.R.I.C.S. economies from 2000 to 2018. The study uses quantile regressions and other advanced techniques to deal with the problems of cross-sectional dependence (C.S.D.) and heterogeneity. The estimated outcomes show that green finance, R.E.U. consumption, and technical innovations perform well in securing the environment by reducing carbon emissions. Likewise, the environmental quality in selected economies is deteriorating due to the rise in non-R.E.U. consumption, economic progress, F.D.I., and trade openness. Therefore, it is time to reshape the local, national and regional growth policies concerning a green investment that can secure our environment. Also, this study proposes future pathways for green finance and other factors relevant to a sustainable environment

ADGym: Design Choices for Deep Anomaly Detection

Deep learning (DL) techniques have recently found success in anomaly detection (AD) across various fields such as finance, medical services, and cloud computing. However, most of the current research tends to view deep AD algorithms as a whole, without dissecting the contributions of individual design choices like loss functions and network architectures. This view tends to diminish the value of preliminary steps like data preprocessing, as more attention is given to newly designed loss functions, network architectures, and learning paradigms. In this paper, we aim to bridge this gap by asking two key questions: (i) Which design choices in deep AD methods are crucial for detecting anomalies? (ii) How can we automatically select the optimal design choices for a given AD dataset, instead of relying on generic, pre-existing solutions? To address these questions, we introduce ADGym, a platform specifically crafted for comprehensive evaluation and automatic selection of AD design elements in deep methods. Our extensive experiments reveal that relying solely on existing leading methods is not sufficient. In contrast, models developed using ADGym significantly surpass current state-of-the-art techniques.Comment: NeurIPS 2023. The first three authors contribute equally. Code available at https://github.com/Minqi824/ADGy