Miracle-dz/UniFAR-FTUs huggingface dataset:
Forced eccentricity in circumbinary discs

We analyse the eccentric response of a low mass coplanar circumbinary disc to secular tidal forcing by a Keplerian eccentric orbit central binary. The disc acquires a forced eccentricity whose magnitude depends on the properties of the binary and disc. The largest eccentricities occur when there is a global apsidal resonance in the disc. The driving frequency by the binary is its apsidal frequency that is equal to zero. A global resonance occurs when the disc properties permit the existence of a zero apsidal frequency free eccentric mode. Resonances occur for different free eccentric modes that differ in the number of radial nodes. For a disc not at resonance, the eccentricity distribution has somewhat similar form to the eccentricity distributions in discs at resonance that have the closest matching disc aspect ratios. For higher disc aspect ratios, the forced eccentricity distribution in a 2D disc is similar to that of the fundamental free mode. The forced eccentricity distribution in a 3D disc is similar to that of higher order free modes, not the fundamental mode, unless the disc is very cool. For parameters close to resonance, large phase shifts occur between the disc and binary eccentricities that are locked in phase. Forced eccentricity may play an important role in the evolution of circumbinary discs and their central binaries.
Tax avoidance, strategic activism and enterprise risk

The paper uses China's A-share listed companies from 2011 to 2020 as a research sample to explore the relationship between strategic radicality and enterprise risk. It is found that strategic radicality is significantly positively correlated with enterprise risk. The more radical the strategy is, the greater the enterprise risk is. Tax avoidance has a negative regulated effect on strategic radicality and enterprise risk due to its effect of alleviating financing constraints. The research results of this paper have strong enlightenment for government regulators and enterprise management.
Development of a Model for the Implementation of Business Intelligence in SMEs

In Switzerland, SMEs account for 99% of the market and employ around two-thirds of all employees. Many of these SMEs already use BI systems and generate business and operational data. Nevertheless, in many cases the potential that could arise from the data is not exploited. As a result, SMEs are falling behind large companies, as empirical studies show. From this, the problem can be derived that despite existing data in Swiss SMEs, the benefit in terms of Business Intelligence (BI) is unrecognized. The goal of this paper is to develop a framework model for the introduction of Business Intelligence in SMEs. The model should be structured generically and show on a meta-level which points are important for the introduction of BI and what the procedure for this is. In this paper we work within a research framework for information systems, using a design science research approach and a method engineering approach for the development of the model. In the development of the model, findings from a previous work were included as well as other studies dealing with the implementation of business intelligence. For the evaluation of the developed model, SMEs as well as business intelligence consultants were interviewed. The answers to the scope of the model were positive. However, there were suggestions to improve the model. Especially for SMEs it is important that business cases are discussed and solutions and their benefits are clearly shown.
Safe return to the workplace: Perceived opportunities and threats in the use of health surveillance technologies in public administrations

The digitalization in public administrations has seen, through the COVID-19 pandemic, the appearance of health surveillance technologies at the workplace. Wearable health devices, such as physiolytics, may then have an increasing role in the management of public agents. Still, little is known about the use of these systems in work settings, as research is mainly oriented towards ethical debates or legal considerations. Accordingly, we propose to consider a concrete case of implementation of physiolytics in a Swiss public administration. We particularly investigate employees’ use rates as well as the perceived opportunities and threats that are linked to physiolytics and health surveillance technologies. This is done through an action design research perspective, where we search to extract from the field guidelines and knowledge for practitioners. We especially highlight that physiolytics’ use steadily decline after the first weeks, due to the design of such devices, the fear of surveillance, and the impression of competition that these systems bring into the workplace. It is therefore vital for public managers to introduce interventions, such as regular feedback, gamification, or nudging to support the engagement of public agents and ensure the viability of such novel health initiatives.
Tamera: Contactless Commodity Tracking, Material and Shopping Behavior Recognition Using COTS RFIDs

RFID technology has recently been exploited for not only identification but also fine-grained trajectory tracking and gesture recognition. While contact-based (a tag is attached to the target of interest) sensing has achieved promising results, contactless sensing still faces severe challenges such as low accuracy and inability to sense multiple targets simultaneously in proximity, restricting its applicability in real-world deployment. In this work, we present Tamera, a contactless RFID-based sensing system, which significantly improves the tracking accuracy, enables multi-commodity tracking, and even material and shopping behavior recognition. We successfully address multiple technical challenges, and design and implement our prototype on commodity RFID devices. We test the positioning accuracy of Tamera in a 5 m × 6 m laboratory. Tamera achieves a median error of 1.3 cm and 2.7 cm for contactless single- and multi-commodity tracking, respectively. In our laboratory, two shelves commonly found in the supermarket are arranged and the goods are placed on them. Tamera successfully localizes and identifies the material type (metal, plastic, paper, and glass) of the commodities on the shelf with an accuracy higher than 95%. Tamera successfully recognizes four shopping behaviors (taking commodity, replacing commodity, buying commodity, and invoking commodity) with an accuracy higher than 93%.
The Verification of Public Health Maturity Framework for E-Government

Is WHO's eHealth model, adapted and adopted by European Union, a viable model to assess country's readiness in using digital technologies in healthcare? Based on the example of the country – its policy documents and existing eHealth services – the paper examines the applicability of the model and ease of assessment. The research objective is to validate the model against existing country's public health system, verifying its applicability and uncover any gaps in the model as well as the framework.
Please stop trying to predict elections only with Twitter

This study began from a genuine desire to discover how using Social Media (SM) data could predict presidential elections worldwide. However, instead of defining a new prediction model or improving one already in existence, we began by studying the fundamentals and evolution of election polling and predictions since before the poll crisis of 1936. We also built on our experience of collecting and analyzing data from many different SM platforms. Lastly, we performed a deep analysis of nine reviews on predicting elections with SM data from 2013 to 2021. We observed that as from 2010, the volume and sentiment analysis based solely on Twitter posts tweeted by ordinary people mentioning candidates and parties was by far the most commonly used approach. However, currently, and contrary to initial expectations, most of the research that uses this approach has been unable to solve many of the challenges encountered since it began in 2010, and also shares many of the characteristics of unsuccessful straw polls performed before 1936. As examples, we highlight the impracticability of polling over Twitter, due to several biases and technical barriers, the need for external data, and the high dependency on the arbitrary decisions of researchers. Lastly, we indicate some of the possible future directions, such as a focus on creating repeatable processes; the use of SM data as part of statistical models, instead of polling; diversifying the input data sources, including multiple SM platforms and non-SM data such as polls and economic indicators; and using machine learning for regression of the vote share, rather than for sentiment analysis.
Decoding the Kodi Ecosystem

Free and open-source media centers are experiencing a boom in popularity for the convenience they offer users seeking to remotely consume digital content. Kodi is today’s most popular home media center, with millions of users worldwide. Kodi’s popularity derives from its ability to centralize the sheer amount of media content available on the Web, both free and copyrighted. Researchers have been hinting at potential security concerns around Kodi, due to add-ons injecting unwanted content as well as user settings linked with security holes. Motivated by these observations, this article conducts the first comprehensive analysis of the Kodi ecosystem: 15,000 Kodi users from 104 countries, 11,000 unique add-ons, and data collected over 9 months. Our work makes three important contributions. Our first contribution is that we build “crawling” software (de-Kodi) which can automatically install a Kodi add-on, explore its menu, and locate (video) content. This is challenging for two main reasons. First, Kodi largely relies on visual information and user input which intrinsically complicates automation. Second, the potential sheer size of this ecosystem (i.e., the number of available add-ons) requires a highly scalable crawling solution. Our second contribution is that we develop a solution to discover Kodi add-ons. Our solution combines Web crawling of popular websites where Kodi add-ons are published (LazyKodi and GitHub) and SafeKodi, a Kodi add-on we have developed which leverages the help of Kodi users to learn which add-ons are used in the wild and, in return, offers information about how safe these add-ons are, e.g., do they track user activity or contact sketchy URLs/IP addresses. Our third contribution is a classifier to passively detect Kodi traffic and add-on usage in the wild. Our analysis of the Kodi ecosystem reveals the following findings. We find that most installed add-ons are unofficial but safe to use. Still, 78% of the users have installed at least one unsafe add-on, and even worse, such add-ons are among the most popular. In response to the information offered by SafeKodi, one-third of the users reacted by disabling some of their add-ons. However, the majority of users ignored our warnings for several months attracted by the content such unsafe add-ons have to offer. Last but not least, we show that Kodi’s auto-update, a feature active for 97.6% of SafeKodi users, makes Kodi users easily identifiable by their ISPs. While passively identifying which Kodi add-on is in use is, as expected, much harder, we also find that many unofficial add-ons do not use HTTPS yet, making their passive detection straightforward.1
Redundant Label Learning via Subspace Representation and Global Disambiguation

Redundant Label Learning (RLL) aims at inducing a robust model from training data, where each example is associated with a set of candidate labels, among which some of them are incorrect. Most existing approaches deal with such problem by disambiguating the candidate labels first and then inducing the predictive model from the disambiguated data. However, these approaches only focus on disambiguation for each instance’ candidate label set, while the global label context tends to be ignored. Meanwhile, these approaches usually induce the objective model by directly utilizing the original feature information, which may lead to the model overfitting due to high-dimensional redundant features. To tackle the above issues, we propose a novel feature SubspacE Representation and label Global DisambiguatIOn (SERGIO) approach, which improves the generalization ability of the learning system from the perspective of both feature space and label space. Specifically, we project the original high-dimensional feature space into a low-dimensional subspace, where the projection matrix is regularized with an orthogonality constraint to make the subspace more compact. Meanwhile, we introduce a label confidence matrix and constrain it with ℓ1-norm and trace-norm regularization simultaneously, which are utilized to explore global label correlations and further well in accordance with the nature of single-label classification and multi-label classification problem, respectively. Extensive experiments on both single-label and multi-label RLL datasets demonstrate that our proposed method achieves competitive performance against state-of-the-art approaches.
Let's Gamify Open Government Data Portals! The GamOGD prototype

Government policies focused on Open Government Data (OGD) often aim to stimulate the provision of public, interoperable data towards any user, including lay citizens, through online portals. However, these OGD portals are primarily developed for expert users. This hinders the realization of transparency, empowerment, and equality of access. This system demonstration paper presents GamOGD, an OGD portal prototype tailored to lay citizens that implements fifteen gamification design propositions.
GRASP: Scalable Graph Alignment by Spectral Corresponding Functions

What is the best way to match the nodes of two graphs? This graph alignment problem generalizes graph isomorphism and arises in applications from social network analysis to bioinformatics. Some solutions assume that auxiliary information on known matches or node or edge attributes is available, or utilize arbitrary graph features. Such methods fare poorly in the pure form of the problem, in which only graph structures are given. Other proposals translate the problem to one of aligning node embeddings, yet, by doing so, provide only a single-scale view of the graph. In this article, we transfer the shape-analysis concept of functional maps from the continuous to the discrete case, and treat the graph alignment problem as a special case of the problem of finding a mapping between functions on graphs. We present GRASP, a method that first establishes a correspondence between functions derived from Laplacian matrix eigenvectors, which capture multiscale structural characteristics, and then exploits this correspondence to align nodes. We enhance the basic form of GRASP by altering two of its components, namely the embedding method and the assignment procedure it employs, leveraging its modular, hence adaptable design. Our experimental study, featuring noise levels higher than anything used in previous studies, shows that the enhanced form of GRASP outperforms scalable state-of-the-art methods for graph alignment across noise levels and graph types, and performs competitively with respect to the best non-scalable ones. We include in our study another modular graph alignment algorithm, CONE, which is also adaptable thanks to its modular nature, and show it can manage graphs with skewed power-law degree distributions.
How Technology makes Institutions: the case of Peru

The impact of ICTs on public sector organizations and the services they produce and deliver has been widely discussed in literature. Attention has been given to how functional properties of ICTs affect efficiency and effectiveness of public services provision and delivery, as well as the impacts they have on the value these services produce. This paper complements this literature exploring how the functions designed into ICTs carry regulatory properties that trigger institutional changes that have pervasive effects on the production and provision of public services. Building on the case of the deployment of the SISFOH system in Peru, the paper discusses how changes in the functional properties of SISFOH modify the institutional arrangement underpinning the production and provision of welfare services in Peru to the point where a new Ministry (MIDIS) is created. The paper offers valuable insights to better address and discuss the complexity of the impacts of ICTs on public sector organizations and the services they provide.
Selection and validation of reference genes for RT-qPCR analysis of different organs at various development stages in Caragana intermedia

Abstract Reverse transcription quantitative PCR (RT-qPCR) is a technique widely used to investigate the expression of genes. An appropriate reference gene (RG) is essential for RT-qPCR analysis to obtain accurate and reliable results. Caragana intermedia plays an important role in afforestation as a bush. However, due to the lack of appropriate RGs, the research on development-related genes is limited. In this study, the selection for suitable RGs of different organs at various development stages to normalize the results of RT-qPCR about development-related genes was performed. To test the expression stability across all samples, we used the software algorithms such as geNorm, NormFinder, BestKeeper, and RefFinder to evaluate all the candidate RGs. Our results showed that CiEF1α was the most stable RG with little fluctuation among all samples. In addition, CiGAPDH in roots, CiSKIP1 in stems and leaves, and CiEF1α in different organs were selected as the most stable RGs. To confirm the applicability of the most stable RGs, the relative expression of CiWRKY17 was normalized using different candidate RGs. Taken together, our research laid a foundation for the study of development-related genes in C. intermedia.
Applying on Node Aggregation Methods to MPI Alltoall Collectives: Matrix Block Aggregation Algorithm

This paper presents algorithms for all-to-all and all-to-all(v) MPI collectives optimized for small-medium messages and large task counts per node to support multicore CPUs in HPC systems. The complexity of these algorithms is analyzed for two metrics: the number of messages and the volume of data exchanged per task. These algorithms have optimal complexity for the second metric, which is better by a logarithmic factor than that in algorithms designed for short messages, with logarithmic complexity for the first metric. It is shown that the balance between these two metrics is key to achieving optimal performance. The performance advantage of the new algorithm is demonstrated at scale by comparing performance versus logarithmic algorithm implementations in Open MPI and Spectrum MPI. The two-phase design for the all-to-all(v) algorithm is presented. It combines efficient implementations for short and large messages in a single framework which is known to be an issue in logarithmic all-to-all(v) algorithms.
Institutional Trust and Social Media Use in Citizen-State Relations: Results from an international cross country vignette study

The objective of this article is to identify whether trust affects citizens’ use of social media to initiate conversations with government on social media platforms. Using a vignette survey, we gathered data from the Canada, Greece, the Netherlands and Paraguay. Multivariate analysis showed that controlling for demographics and individual-level adoption factors, trust in government does not impact citizens’ use of social media to initiate conversations about public issues, but trust in social media business and organizational infrastructure is (both in democratic countries as well as in flawed democracies). These results highlight how trust in institutions affect citizens’ engagement and digital participation, and identifies conditions under which social media platforms may contribute to a vibrant democracy.
In silico labeling enables kinetic myelination assay in brightfield

Recent advances with deep neural networks have shown the feasibility of acquiring brightfield images with transmitted light and applying in-silico labeling to predict fluorescent images. We have developed a novel in-silico labeling method based on a generative adversarial network and outperforms the state-of-the-art Unet method in generating realistic fluorescent images and quantitatively recapitulating real staining signals, as demonstrated in a complex co-culture myelination assay. Furthermore, we have performed the assay in live mode with multiple kinetic points, applied in-silico labeling to predict fluorescent images from brightfield and quantified the kinetic phenotypic changes. Thus, the proposed approach provides a potential tool to study the kinetics of cellular phenotypic changes with brightfield imaging.
Phosphorylation of Gαi shapes canonical Gα(i)βγ/GPCR signaling

A long-standing question in the field of signal transduction is to understand the interplay between distinct signaling pathways that control cell behavior. For growth factors and heterotrimeric G proteins, the two major signaling hubs in eukaryotes, the mechanisms of independent signal transduction have been extensively characterized; however, if/how they may cross talk remains obscure. Here we use linear-ion-trap mass spectrometry in combination with cell-based biophysical, biochemical, and phenotypic assays to chart at least three distinct ways in which growth factors may impact canonical Gα(i)βγ signaling downstream of a GPCR (CXCR4) via phosphorylation of Gαi. Phosphomimicking mutations in a cluster of residues in the αE helix (Y154/Y155) result in the suppression of agonist-induced Gα(i) activation while promoting constitutive Gβγ signaling; others in the P-loop (Ser44, Ser47, Thr48) suppress Gi activation entirely thus completely segregating the growth factor and GPCR pathways. While most phosphoevents appear to impact, as expected, the core properties of Gα(i) (conformational stability, nucleotide binding, Gβγ association and release, etc.), one phosphomimicking mutation promoted mislocalization of Gαi from the plasma membrane: a novel and unexpected mechanism of GPCR signal suppression. A phosphomutation of C-terminal Y320 was sufficient to orchestrate such suppression by protein compartmentalization. Findings not only elucidate how growth factor and chemokine signals crosstalk through phosphomodulation of Gαi, but also how such crosstalk may generate signal diversity.