Miracle-dz/UniFAR-FTUs huggingface dataset:
ASAP: Fast Mobile Application Switch via Adaptive Prepaging

With mobile applications’ ever-increasing demands for memory capacity, along with a steady increase in the number of applications running concurrently, memory capacity is becoming a scarce resource on mobile devices. When the memory pressure is high, current mobile OSes often kill application processes that have not been used recently to reclaim memory space. This leads to a long delay when a user relaunches the killed application, which degrades the user experience. Even if this mechanism is disabled to utilize a compression-based in-memory swap mechanism, relaunching the application still incurs a substantial latency penalty as it requires the decompression of compressed anonymous pages and a stream of I/O accesses to retrieve ﬁle-backed pages into memory. This paper identiﬁes conventional demand paging as the primary source of this inefﬁciency and proposes ASAP, a mechanism for fast a pplication s witch via a daptive p repaging on mobile devices. ASAP performs prepaging by combining i) high-precision switch footprint estimators for both ﬁle-backed and anonymous pages, and ii) efﬁcient implementation of the prepaging mechanism to minimize resource waste for CPU cycles and disk bandwidth during an application switch. Our evaluation using eight real-world applications on Google Pixel 4 and Pixel 3a demonstrates that ASAP can reduce the switch time by 22.2% and 28.3% on average, respectively (with a maximum of 33.3% and 35.7%, respectively), over the vanilla Android 10.
Bank Loan Undrawn Spreads and the Predictability of Stock Returns

We document a novel empirical finding that the private information contained in bank loans’ undrawn spreads can predict firms’ future stock returns across a range of time horizons. This effect is separate from previously documented asset pricing puzzles related to idiosyncratic volatility, analyst forecast dispersion, and credit risk. Further investigations show that our private information measure could predict firms’ future cash flow uncertainty and cash flow level, which may potentially explain the negative relation between this measure and future stock returns. Notably, a long-short strategy based on this finding can generate a significant alpha of around 7% per year. JEL Classifications: G12, G20.
Valuation of Drivers and Barriers to Smart Meter Adoption in the UK: A Survey Experiment∗

To facilitate a sustainable energy transition, governments and innovators have encouraged the adoption of smart technologies in the home that allow for increased flexibility in centralized energy grids. The ambitious Smart Meter Implementation Programme in the United Kingdom has indisputably failed to achieve its objective of equipping all UK dwellings with smart meters by 2020, perhaps due to some or all of several identified barriers to adoption of allegedly welfare-enhancing energy technology in the home. By partnering with the UK’s energy regulator, this research uses an incentive-compatible online experiment to elicit the willingness-to-accept of a representative panel of over 2,400 UK households for smart meter installation. Randomized information treatments allow for assessment of the impact on adoption and willingness-to-accept of several purported market failures in relation to smart meter adoption, namely information asymmetries regarding the personal and social benefits of smart meter adoption as well as information regarding accumulated positive ‘learning-by-using’ externalities. We explore treatment effects for a range of potential subsidy values, and discuss implications for policymakers in encouraging residential smart meter adoption. ∗We are grateful to Ofgem—particularly Moira Nicolson and Amy O’Mahoney—for their work in facilitating interaction with energy suppliers. We thank ENABLE.EU and the ESRC Centre for Climate Change Economics and Policy (CCCEP) for their financial support of the research. Gosnell is a beneficiary of an AXA Research Fund postdoctoral grant. This research is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie grant agreement No. 681228. The pre-registry for this project can be found on the Open Science Framework. †Grantham Research Institute, London School of Economics; corresponding author email: g.gosnell@lse.ac. uk
Does it pay to be nice? Personality and earnings in the UK

This study uses data from the British Household Panel Study (BHPS) and employs all basic traits from the Five Factor Personality Inventory – openness to experience, conscientiousness, extraversion, agreeableness and neuroticism – to examine the relationship between individuals’ personality and wages in the UK. The results indicate a negative linear relationship between wages and agreeableness and, for females, wages and neuroticism whereas openness to experience is rewarded. There however are nonlinear gradients between wages and conscientiousness, and wages and extraversion. There further are no joint effects of personality and tenure, and there is evidence for a stronger causal pathway for females. JEL classification: J31
Single Cell Center of Mass for the Analysis of BMP Receptor Heterodimers Distributions

At the plasma membrane, transmembrane receptors are at the interface between cells and their environment. They allow sensing and transduction of chemical and mechanical extracellular signals. The spatial distribution of receptors and the specific recruitment of receptor subunits to the cell membrane is crucial for the regulation of signaling and cell behavior. However, it is challenging to define what regulates such spatial patterns for receptor localization, as cell shapes are extremely diverse when cells are maintained in standard culture conditions. Bone morphogenetic protein receptors (BMPRs) are serine-threonine kinases, which build heteromeric complexes of BMPRI and II. These are especially interesting targets for receptor distribution studies, since the signaling pathways triggered by BMPR-complexes depends on their dimerization mode. They might exist as preformed complexes, or assemble upon binding of BMP, triggering cell signaling which leads to differentiation or migration. In this work we analyzed BMPR receptor distributions in single cells grown on micropatterns, which allow not only to control cell shape, but also the distribution of intracellular organelles and protein assemblies. We developed a script called ComRed (Center Of Mass Receptor Distribution), which uses center of mass calculations to analyze the shift and spread of receptor distributions according to the different cell shapes. ComRed was tested by simulating changes in experimental data showing that shift and spread of distributions can be reliably detected. Our ComRed-based analysis of BMPR-complexes indicates that receptor distribution depends on cell polarization. The absence of a coordinated internalization after addition of BMP suggests that a rapid and continual recycling of BMPRs might occur. Receptor complexes formation and localization in cells induced by BMP might yield insights into the local regulation of different signaling pathways.
Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study

Background Mental health problems are widely recognized as a major public health challenge worldwide. This concern highlights the need to develop effective tools for detecting mental health disorders in the population. Social networks are a promising source of data wherein patients publish rich personal information that can be mined to extract valuable psychological cues; however, these data come with their own set of challenges, such as the need to disambiguate between statements about oneself and third parties. Traditionally, natural language processing techniques for social media have looked at text classifiers and user classification models separately, hence presenting a challenge for researchers who want to combine text sentiment and user sentiment analysis. Objective The objective of this study is to develop a predictive model that can detect users with depression from Twitter posts and instantly identify textual content associated with mental health topics. The model can also address the problem of anaphoric resolution and highlight anaphoric interpretations. Methods We retrieved the data set from Twitter by using a regular expression or stream of real-time tweets comprising 3682 users, of which 1983 self-declared their depression and 1699 declared no depression. Two multiple instance learning models were developed—one with and one without an anaphoric resolution encoder—to identify users with depression and highlight posts related to the mental health of the author. Several previously published models were applied to our data set, and their performance was compared with that of our models. Results The maximum accuracy, F1 score, and area under the curve of our anaphoric resolution model were 92%, 92%, and 90%, respectively. The model outperformed alternative predictive models, which ranged from classical machine learning models to deep learning models. Conclusions Our model with anaphoric resolution shows promising results when compared with other predictive models and provides valuable insights into textual content that is relevant to the mental health of the tweeter.
Classiﬁcation and Clustering on Schizophrenic Patients data

—Current diagnosis and treatment methods for some psychiatric conditions like schizophrenia have shown their limits. Indeed, those procedures are still symptom-based as the biological mechanisms that underlie the disorder are not well understood yet. Machine learning is a ﬁeld that could help scientists ﬁnd what causes schizophrenia. In the present article, we try classiﬁcation algorithms on schizophrenic patients, their ﬁrst-degree relatives and healthy controls to learn more about the diagnosis aspect. We also apply clustering methods on schizophrenic patients data to try to transcend the current schizophrenia categories with new suspected subtypes of the disease.
Predictable Reversals: Credit Booms, Debt Service and Prolonged Slumps∗

Traditional economic models have had di culty explaining why booms in household credit have predictable negative after-e ects that may last for up to a decade. This paper explains why: when taking on new debt, borrowers commit to a pre-speci ed path of future debt service. We rst show theoretically that there are two key properties of the data that give rise to a pronounced lag between credit booms and debt service: (i) that new borrowing is strongly auto-correlated and (ii) that debt contracts are long term. Then we analyze a panel of household debt in 17 countries and nd that on average, the lag between peaks in credit booms and peaks in debt service is four years. Furthermore, we show that this delayed increase in debt service explains why credit booms are associated with lower future output growth and higher probability of crisis for up to seven years. Our results thus provide a systematic transmission mechanism from credit expansions to prolonged adverse real e ects, highlighting a signi cant challenge for macroeconomic management. JEL Codes: E17, E44, G01, D14
Adapting the thermal-based two-source energy balance model to estimate daytime turbulent fluxes in a complex tree-grass ecosystem

The thermal-based Two-Source Energy Balance (TSEB) model has accurately simulated energy fluxes in a wide range of landscapes. However, tree-grass ecosystems (TGE) have notably complex heterogenous vegetation mixtures and 20 dynamic phenological characteristics presenting clear challenges to earth observation and modeling methods. The TSEB model was tested in a TGE ecosystem and an adaptation was proposed to consider spatial and temporal complexity. This was based on sensitivity analyses (SA) conducted on both primary remote sensing inputs (local SA) and model parameters (global SA). The model was subsequently modified taking into account phenological dynamics and assuming a dominant vegetation structure and cover (i.e. either grassland or broadleaved trees) for different seasons (TSEB-2S). The adaptation 25 was compared against the default (i.e. non-seasonally changing) model and evaluated against eddy covariance (EC) flux measurements and lysimeters over a TGE experimental site in central Spain. TSEB-2S vastly improved over the default TSEB performance decreasing the mean bias and RMSD of LE from 34 and 77 W m -2 to 0 and 59 W m -2 , respectively during 2015. TSEB-2S was further validated for two other EC towers and for different years (2015, 2016 and 2017) obtaining similar error statistics with RMSE of LE ranging between 51 and 63 W m -2 . The results presented here 30 demonstrate the important role that vegetation, through its structure and phenology, has in controlling ecosystem level energy fluxes, which become important considerations for the modeling procedure. Additionally, TSEB was shown to be most sensitive to parameters related to radiation partitioning between canopy and soil, such as characterizing vegetation clumping, and parameters related to vegetation structure involved in quantifying the resistance to turbulent flow. 35
High Performance Conditioning Shampoo with Hyaluronic Acid and Sustainable Surfactants

Recently, consumers have become invested in more natural and sustainable ingredients contained in personal care products. Unfortunately, cationic surfactants are still heavily relied on as primary conditioning agents in products such as conditioning shampoos because of their ability to cling well to the negatively charged surface of hair follicles. Additionally, sulfates are utilized as cleansing agents because they are highly effective and low cost. The objective of this study is to find a more sustainable formulation for a conditioning shampoo without compromising the desired wet combing, rheological, and surface activity properties. The systems which were investigated contained hyaluronic acid (HA) at a variety of molecular weights and concentrations, in combination with a surfactant, either acidic sophorolipid (ASL) or alkyl polyglucoside (APG), and varying the presence of sodium chloride. A Dia-stron was utilized to test the wet combing force, a rheometer recorded the viscosity at various shear rates, and a tensiometer measured the surface tension of the samples before a visual foaming study was conducted. Molecular weight and concentration seemed to have a large impact on wet combing force, as well as rheology, with the largest molecular weight and concentration producing the lowest friction coefficient and desired rheological profile. The addition of a surfactant significantly aids in the reduction in surface tension and increased foamability. Therefore, the optimal system to achieve the largest reduction in wet combing force, large viscosity with shear-thinning behavior, and relatively low surface tension with decent foaming is composed of 1% HA at 800 kDa, 10% ASL and 1% NaCl. This system shows a viable sulfate-free and silicone-free option that can achieve both conditioning and cleansing.
Stratospheric ozone loss-induced cloud effects lead to less surface ultraviolet radiation over the Siberian Arctic in spring

Surface ultraviolet (UV) radiation has important effects on human health, terrestrial and aquatic ecosystems, and air pollution. Both stratospheric ozone and clouds are key factors that influence surface UV radiation. Here we find that Arctic ozone loss may lead to a decrease in surface UV radiation over the Siberian Arctic in spring using ERA5 reanalysis. It is found that Arctic ozone loss is associated with an increase in high clouds by modifying static stability in the upper troposphere. Stratospheric ozone loss allows more UV radiation to reach the surface. On the contrary, the increase in high clouds results in a reduction of surface UV radiation. Interestingly, a composite analysis suggests that this cloud masking effect is found to be stronger than that from stratospheric ozone loss over the Siberian Arctic in spring. These results suggest that we should pay more attention to the high-ozone events which would lead to more surface UV radiation by the cloud effects.
A Survey on Various Deep Learning Models for Automatic Image Captioning

Automatic Image captioning means the generation of a caption for an image by a machine. Image captioning is performed by recognizing objects, attributes and interconnection between them. This task involves computer vision for image understanding, natural language processing for syntax and semantics purpose and machine learning for caption generation. Preferably CNN is used to understand features of an image and RNN is used for sentence generation. Earlier, Machine learning approach was used for this purpose. Input data is used to extract the features in traditional machine learning. Extracting features like handcrafted from large dataset is not so easy and feasible. Later on, Various deep learning-based approaches were proposed. In deep learning, retrieval based and template-based methods were proposed but faced some issues like missing important objects and fixed length caption respectively. Then end to end learning approach based on deep learning network came into existence and image captioning task became more efficient. The objective of this paper is to study and compare various end to end learning-based framework for image captioning using standard evaluation metric and to understand how can these frameworks be used for various research applications. Along with the comparison, futuristic challenges have also been discussed.
An Empirical Study for the Deep Learning Models

Deep Learning (DL) models have tested to be very powerful in solving many hard problems. Especially, those are related to computer vision, text, speech, and classification. However, the blueprint of such models requires large space and elaboration that needs to be examined. Convolutional Neural Network (CNN) is the most popular neural network that can extract the features automatically as compared to conventional machine learning algorithms (CMLA). Our aim in this paper is to lessen the human attempt required to layout architectures by the use of a gadget architecture development process that allows the exploration of huge design space by automating sure version construction, alternative generation, and assessment. The main operations in CNN are Convolution, Pooling, Flattening, Full Connection between the input and output layer. The dataset taken as CIFAR 10 having 60,000 color images of 10 different classes is considered for the study where, various classes represent the images of cars, trucks, frogs, horses, trucks, cats, cars, airplanes, ships, and deer. It is expected that the performance of the CNN model can be further improved by using the deeper network architecture, or by an increasing number of epochs or data augmentation. In this paper, an attempt has been made to explain simple and deeper CNN models on the CIFAR 10 dataset and the comparison has been carried out to check the accuracy achieved from both the models.
Hyperspectral Image Classification Using Deep Learning Models: A Review

Hyperspectral image (HSI) classification is one of the important topic in the field of remote sensing. In general, HSI has to deal with complex characteristics and nonlinearity among the hyperspectral data which makes the classification task very challenging for traditional machine learning (ML) models. Recently, deep learning (DL) models have been very widely used in the classification of HSIs because of their capability to deal with complexity and nonlinearity in data. The utilization of deep learning models has been very successful and demonstrated good performance in the classification of HSIs. This paper presents a comprehensive review of deep learning models utilized in HSI classification literature and a comparison of various deep learning strategies for this topic. Precisely, the authors have categorized the literature review based upon the utilization of five most popular deep learning models and summarized their main methodologies used in feature extraction. This work may provide useful guidelines for the future research work in this area.
Analysis of alcohol abuse using improved artificial intelligence methods

In this work, artificial intelligence (AI) based method has been proposed for analysis of alcohol abuse (AA) to evaluate drug risk and find an optimal method for analysis of AA. To detect alcohol abuse, twelve input features are selected. As the number of positive classes and negative classes are imbalanced, input features are processed using synthetic minority oversampling technique (SMOTE). The selected input features are given to the AI based method to predict whether an individual uses alcohol or not. Three AI methods (logistic regression, Naïve Bayes and gradient boost) have been used from which the most suitable method is chosen as optimal method for analysis of AA. Methods used for validation of the proposed AI based method are leave one out cross validation and test sample estimate. Among the three methods used, gradient boost method performs better than other method. Gradient Boost based AI method has 97.55% accuracy to predict alcohol user. Significant achievement of the proposed work is that the accuracy (nearly 97%) to detect alcohol abuse is much more than previously (nearly 70%) suggested methods. This is due to the efficient design of AI method implemented in this work. All the feature processing work and AI algorithm design work has been done using python software.
Latest trend in building construction: three-dimensional printing

Today world is looking towards additive manufacturing as a solution for sustainability in the construction business, one of which is three-dimensional printing that involves creating a three-dimensional (3-D) object by printing layer by layer developed digitally. Nowadays, 3-D printers have evolved so much that they can create a whole house from a pin. This technology is not only cost and time effective but also speeds up the construction rate with reduced power, material consumption, and hence reduced waste in building construction. The scope, elements, and classification of 3D printing has been discussed to highlight the work done by past researchers. This paper reviews the elements, merits, and demerits of using 3D printing in construction and evaluates it as a promising technique for sustainable development.
Growth of relaxed GeSn film with high Sn content via Sn component-grade buffer layer structure

Relaxed GeSn films with a high Sn content (>13%) were grown on Ge (100) substrates via magnetron sputtering epitaxy through a component-grade GeSn buffer layer structure. Raman spectroscopy, high-resolution x-ray diffraction, and cross-sectional transmission electron microscopy revealed that Ge0.861Sn0. 139 alloys, with a degree of strain relaxation of up to 96.1%, can be achieved through a component-grade buffer layer structure. The threading dislocation density was estimated to be around 2×108 cm−2. The atomic surface flatness of the GeSn alloys was confirmed by atomic force microscopy, and the surface roughness was observed to be below 1 nm. Moreover, the thermal stability of GeSn samples was investigated. The results indicated that the component-grade structure is effective for realizing a high Sn composition and high relaxation of the GeSn alloy.