Miracle-dz/UniFAR-FTUs huggingface dataset:
Covariant entropy bound beyond general relativity

We propose a covariant entropy bound in gravitational theories beyond general relativity (GR), using Wald-Jacobson-Myers entropy instead of Bekenstein-Hawking entropy. We first extend the proof of the bound known in 4-dimensional GR to $D$-dimensional GR, $f(R)$ gravity and canonical scalar-tensor theory. We then consider Einstein-Gauss-Bonnet (EGB) gravity as a more non-trivial example and, under a set of reasonable assumptions, prove the bound in the GR branch of spherically symmetric configurations. As a corollary, it is shown that under the null and dominant energy conditions, the generalized second law holds in the GR branch of spherically symmetric configurations of EGB gravity at the fully nonlinear level.
Characterizing the Effect of Sentence Context on Word Meanings: Mapping Brain to Behavior

Semantic feature models have become a popular tool for prediction and interpretation of fMRI data. In particular, prior work has shown that differences in the fMRI patterns in sentence reading can be explained by context-dependent changes in the semantic feature representations of the words. However, whether the subjects are aware of such changes and agree with them has been an open question. This paper aims to answer this question through a human-subject study. Subjects were asked to judge how the word change from their generic meaning when the words were used in specific sentences. The judgements were consistent with the model predictions well above chance. Thus, the results support the hypothesis that word meaning change systematically depending on sentence context.
Toxin of Pyrenophora tritici-repentis: host specificity, significance in disease, and inheritance of host reaction.

Pyrenophora tritici-repentis differentially induces combinations of tan necrosis and extensive chlorosis in individual susceptible wheat cultivars. (…). Segregation of F 2 populations from four different crosses between cultivars resistant and susceptible to tan necrosis indicated that susceptibility to the fungus and sensitivity to the toxin were controlled by the same dominant gene. Toxin production by the pathogen was associated with the ability of individual isolates to induce tan necrosis (nec+) in necrosis-expressing cultivars
Forecasting COVID-19 Pandemic in Mozambique and Estimating Possible Scenarios.

COVID-19 is now the largest pandemic crisis of this century, with over 16 million registered cases worldwide. African countries have now begun registering an increasing number of cases, yet, not many models developed focus in specific African countries. In our study we use a simple SEIR model to evaluate and predict future scenarios regarding the pandemic crisis in Mozambique. We compare the effect of different policies on the infection curve and estimate epidemiological parameters such as the current infection reproduction number Rt and the growth rate g. We have found a low value for Rt, ranging from 1.11 to 1.48 and a positive growth rate, between g = 0.22 to 0.27. Our simulations also suggest that a lockdown shows potential for reducing the infection peak height in 28%, on average, ranging from 20 to 36%.
Quantum Embedded Superstates

Optical supercavity modes (superstates), i.e., hybrid modes emerging from the strong coupling of two modes of an open cavity, can support ultranarrow lines in their scattering spectra associated with quasi bound states in the continuum (quasi‐BIC). These modes are of great interest for sensing applications as they enable compact systems with unprecedented sensitivity. However, classical quasi‐BIC sensors are fundamentally limited by the shot‐noise limit, which may be overcome in quantum sensors. Here, it is shown that a three‐level quantum system (e.g., atom, quantum dot, superconducting qubit) can be tailored to support the quantum analog of superstates with an unboundedly narrow emission line. Remarkably, it is demonstrated that the coupling of such a system with a cavity (e.g., plasmonic or dielectric nanoparticle, microcavity, microwave resonator) enables sensing properties with excellent statistical features. The results can be applied to a plethora of quantum platforms, from superconducting circuits to cold atoms and quantum dots, opening exciting opportunities for quantum sensing and computing.
Multi-Frequency Multi-Scenario Millimeter Wave MIMO Channel Measurements and Modeling for B5G Wireless Communication Systems

Millimeter wave (mmWave) bands have been utilized for the fifth generation (5G) communication systems and will no doubt continue to be deployed for beyond 5G (B5G). However, the underlying channels are not fully investigated at multi-frequency bands and in multi-scenarios by using the same channel sounder, especially for the outdoor, multiple-input multiple-output (MIMO), and vehicle-to-vehicle (V2V) conditions. In this paper, we conduct multi-frequency multi-scenario mmWave MIMO channel measurements with $4\times 4$ antennas at 28, 32, and 39 GHz bands for three cases, i.e., the human body and vehicle blockage measurements, outdoor path loss measurements, and V2V measurements. The channel characteristics, including blockage effect, path loss and coverage range, and non-stationarity and spatial consistency, are thoroughly studied. The blockage model, path loss model, and time-varying channel model are proposed for mmWave MIMO channels. The channel measurement and modeling results will be of great importance for further mmWave communication system deployments in indoor hotspot, outdoor, and vehicular network scenarios for B5G.
Developing a measure of online wellbeing and user trust

This paper describes the first stage of the ongoing development of two scales to measure online wellbeing and trust, based on the results of a series of workshops with younger and older adults. The first, the Online Wellbeing Scale includes subscales covering both psychological, or eudaimonic, wellbeing and subjective, or hedonic, wellbeing, as well as digital literacy and online activity; the overall aim is to understand how a user's online experiences affect their wellbeing. The second scale, the Trust Index includes three subscales covering the importance of trust to the user, trusting beliefs, and contextual factors; the aim for this scale is to examine trust in online algorithm-driven systems. The scales will be used together to aid researchers in understanding how trust (or lack of trust) relates to overall wellbeing online. They will also contribute to the development of a suite of tools for empowering users to negotiate issues of trust online, as well as in designing guidelines for the inclusion of trust considerations in the development of online algorithm-driven systems. The next step is to release the prototype scales developed as a result of this pilot in a large online study in to validate the measures.
openXDATA: A Tool for Multi-Target Data Generation and Missing Label Completion

A common problem in machine learning is to deal with datasets with disjoint label spaces and missing labels. In this work, we introduce the openXDATA tool that completes the missing labels in partially labelled or unlabelled datasets in order to generate multi-target data with labels in the joint label space of the datasets. To this end, we designed and implemented the cross-data label completion (CDLC) algorithm that uses a multi-task shared-hidden-layer DNN to iteratively complete the sparse label matrix of the instances from the different datasets. We apply the new tool to estimate labels across four emotion datasets: one labeled with discrete emotion categories (e.g., happy, sad, angry), one labeled with continuous values along arousal and valence dimensions, one with both kinds of labels, and one unlabeled. Testing with drop-out of true labels, we show the ability to estimate both categories and continuous labels for all of the datasets, at rates that approached the ground truth values. openXDATA is available under the GNU General Public License from this https URL.
Word embedding and neural network on grammatical gender - A case study of Swedish

We analyze the information provided by the word embeddings about the grammatical gender in Swedish. We wish that this paper may serve as one of the bridges to connect the methods of computational linguistics and general linguistics. Taking nominal classification in Swedish as a case study, we first show how the information about grammatical gender in language can be captured by word embedding models and artificial neural networks. Then, we match our results with previous linguistic hypotheses on assignment and usage of grammatical gender in Swedish and analyze the errors made by the computational model from a linguistic perspective.
Preparation and preliminary bioevaluation of a 99mTc(CO)3-glucose derivative prepared by a click chemistry route

Development of a 99mTc-labelled glucose derivative as a single-photon emission computed tomography analogue to [18F]-2-fluoro-2-deoxy-D-glucose (FDG) is considered of great interest. Herein, we present the synthesis and preliminary bioevaluation of a 99mTc(CO)3-glucose derivative. Derivatization of glucose at C2 was achieved using the so-called ‘click chemistry’, forming a histidine-like, 1,4-disubstituted triazole adequate as donor atom system for Tc(I)-tricarbonyl complexes. Synthesis of the intermediate azide derivative was achieved by conventional organic chemistry. Because of the efficiency and selectivity of this cycloaddition, labelling was performed in ‘one pot’, adding the tricarbonyl precursor fac-[99mTc(CO)3(H2O)3]+ to the same vial where the click reaction has taken place without any further purification. A single product with radiochemical purity higher than 90% was obtained. It was stable for at least 4 h in reaction milieu and exhibited high hidrophilicity (log P of −1.2) and low binding to plasma proteins (5 ± 1%). Biodistribution in C57BL/6 mice bearing induced Lewis murine lung carcinoma is characterized by low blood and liver uptake and rapid urinary excretion. 99mTc complex showed moderate tumour uptake but significant retention until 2 h post-injection. Soft tissue clearance was fast leading to significantly higher uptake in tumour in comparison to muscle (p = 0.05) at all time points. Overall, biodistribution of our compound was very similar to that of [18F]-FDG. However, tumour uptake was significantly higher for [18F]-FDG, probably because of high hidrophilicity of our derivative that may hinder cell penetration. Similarity to biodistribution of FDG is a promising outcome, and modifications of the chelator and linker might improve biological results. Copyright © 2012 John Wiley & Sons, Ltd.
An Ensemble Scheme for Proactive Data Allocation in Distributed Datasets

The advent of the Internet of Things (IoT) gives the opportunity to numerous devices to interact with their environment, collect and process data. Data are transferred, in an upwards mode, to the Cloud through the Edge Computing (EC) infrastructure. A high number of EC nodes become the hosts of distributed datasets where various processing activities can be realized in close distance with end users. This approach can limit the latency in the provision of responses. In this paper, we focus on a model that proactively decides where the collected data should be stored in order to maximize the accuracy of datasets present at the EC infrastructure. We consider that the accuracy is defined by the solidity of datasets exposed as the statistical resemblance of data. We argue upon the similarity of the incoming data with the available datasets and select the most appropriate of them to store the new information. For alleviating processing nodes from the burden of a continuous, complicated statistical processing, we propose the use of synopses as the subject of the similarity process. The incoming data are matched against the available synopses based on an ensemble scheme, then, we select the appropriate host to store them and perform the update of the corresponding synopsis. We provide the description of the problem and the formulation of our solution. Our experimental evaluation targets to reveal the performance of the proposed approach.
Flower: A Friendly Federated Learning Research Framework

Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud. However, FL is difficult to implement realistically, both in terms of scale and systems heterogeneity. Although there are a number of research frameworks available to simulate FL algorithms, they do not support the study of scalable FL workloads on heterogeneous edge devices. In this paper, we present Flower -- a comprehensive FL framework that distinguishes itself from existing platforms by offering new facilities to execute large-scale FL experiments and consider richly heterogeneous FL device scenarios. Our experiments show Flower can perform FL experiments up to 15M in client size using only a pair of high-end GPUs. Researchers can then seamlessly migrate experiments to real devices to examine other parts of the design space. We believe Flower provides the community with a critical new tool for FL study and development.
Simultaneous Existence of Ultrahigh and Ultralow Spectral Sensitivity for Directional Couplers

We present the experimental evidence for the existence of two very unique wavelengths, termed critical and cross-over wavelength in the transmission spectrum of the fiber-optic directional coupler, whose properties are conjugate to each other. The spectral shift associated with the transmission maxima/minima suddenly flip around these wavelengths and the spectrum shows largest (nil) spectral shift for the transmission maxima/minima closest to the critical (cross-over) wavelength corresponding to the same external perturbation parameter. We also present a theoretical explanation of the observed experimental behavior, showing that the underlying mechanisms for the existence of these wavelengths are entirely different. The knowledge of the precise spectral location of these wavelengths is necessary to avoid false alarms and for designing extremely sensitive fiber optic sensors.
Dispersion Measures of Fast Radio Burst Host Galaxies Derived from IllustrisTNG Simulation

We calculate the dispersion measures (DMs) contributed by host galaxies of fast radio bursts (FRBs). Based on a few host galaxy observations, a large sample of galaxies with similar properties to observed ones has been selected from the IllustrisTNG simulation. They are used to compute the distributions of host galaxy DMs for repeating and nonrepeating FRBs. For repeating FRBs, we infer the DMhost for FRBs like FRB 121102 and FRB 180916 by assuming that the burst sites are tracing the star formation rates in host galaxies. The median DMshost are 35 (1 + z)1.08 and 96(1 + z)0.83 pc cm−3 for FRBs like FRB 121102 and FRB 180916, respectively. In another case, the median of DMhost is about 30–70 pc cm−3 for nonrepeating FRBs in the redshift range z = 0.1–1.5, assuming that the burst sites are the locations of binary neutron star mergers. In this case, the evolution of the median DMhost can be calculated by 33(1 + z)0.84 pc cm−3. The distributions of DMhost of repeating and nonrepeating FRBs can be well fitted with the log-normal function. Our results can be used to infer redshifts of nonlocalized FRBs.
Information Behavior During the Covid-19 Crisis in German-Speaking Countries

PurposeThe purpose of this study is to explore the impact of the Covid-19 crisis at the level of individual information behavior among citizens from the German-speaking countries, Austria, Germany and Switzerland.Design/methodology/approachAn online survey was conducted among 308 participants gathered through convenience sampling in April and May 2020, focusing on how citizens changed their mix and usage intensity of information sources and according to which criteria they chose them during the Covid-19 crisis. A Wilcoxon signed-rank test was used for testing central tendencies. Effect sizes were considered to support the interpretation.FindingsThe results show first that the Covid-19 crisis has led to an increased demand for reliable information. This goes alongside a significant increased use of public broadcasting, newspapers and information provided by public organizations. Second, the majority (84%) of the participants reported being satisfied with the information supply during the Covid-19 crisis. Participants who were less satisfied with the information supply used reliable sources significantly less frequently, specifically public television, national newspapers and information provided by public organizations. Third, the amount of Covid-19-related information led some participants to a feeling of information overload, which resulted in a reduction of information seeking and media use.Originality/valueThis paper is one of the first to analyze changes of information behavior patterns of individuals during crises in the current information environment, considering the diversity of resources used by individuals.
ECNU-SenseMaker at SemEval-2020 Task 4: Leveraging Heterogeneous Knowledge Resources for Commonsense Validation and Explanation

This paper describes our system for SemEval-2020 Task 4: Commonsense Validation and Explanation (Wang et al., 2020). We propose a novel Knowledge-enhanced Graph Attention Network (KEGAT) architecture for this task, leveraging heterogeneous knowledge from both the structured knowledge base (i.e. ConceptNet) and unstructured text to better improve the ability of a machine in commonsense understanding. This model has a powerful commonsense inference capability via utilizing suitable commonsense incorporation methods and upgraded data augmentation techniques. Besides, an internal sharing mechanism is cooperated to prohibit our model from insufficient and excessive reasoning for commonsense. As a result, this model performs quite well in both validation and explanation. For instance, it achieves state-of-the-art accuracy in the subtask called Commonsense Explanation (Multi-Choice). We officially name the system as ECNU-SenseMaker. Code is publicly available at https://github.com/ECNU-ICA/ECNU-SenseMaker.
Universal Fake News Collection System using Debunking Tweets

Large numbers of people use Social Networking Services (SNS) for easy access to various news, but they have more opportunities to obtain and share ``fake news'' carrying false information. Partially to combat fake news, several fact-checking sites such as Snopes and PolitiFact have been founded. Nevertheless, these sites rely on time-consuming and labor-intensive tasks. Moreover, their available languages are not extensive. To address these difficulties, we propose a new fake news collection system based on rule-based (unsupervised) frameworks that can be extended easily for various languages. The system collects news with high probability of being fake by debunking tweets by users and presents event clusters gathering higher attention. Our system currently functions in two languages: English and Japanese. It shows event clusters, 65\% of which are actually fake. In future studies, it will be applied to other languages and will be published with a large fake news dataset.
A high coverage view of the thermodynamics and metal abundance in the outskirts of the nearby galaxy cluster Abell 2199

We present a joint Suzaku and XMM–Newton analysis of the outskirts of the nearby galaxy cluster Abell 2199, the only nearby galaxy cluster to be observed with near complete azimuthal coverage with Suzaku. Using the XMM–Newton observations to correct for the effects of gas clumping, we find that the azimuthally averaged entropy profile in the outskirts follows a power law with a slope of 1.20 ± 0.23, statistically consistent with a slope of 1.1 predicted by non-radiative simulations for purely gravitational hierarchical structure formation. However, when divided into 10 sectors, the entropy shows significant azimuthal variation, with some sectors lying below the baseline level. The azimuthally averaged gas mass fraction is found to agree with the cosmic mean baryon fraction. The metal abundance in the outskirts is found to be consistent with being uniform in all directions and it has an average value of $0. 29_{-0.03}^{+0.03}\, \mathrm{ Z}_{\odot }$, consistent with the gas accreting on to clusters being pre-enriched with metals.
KELT-17: a chemically peculiar Am star and a hot-Jupiter planet

Context. There is very little information to be found in the literature regarding the detection of planets orbiting chemically peculiar stars. Aims. Our aim is to determine the detailed chemical composition of the remarkable planet host star KELT-17. This object hosts a hot-Jupiter planet with 1.31 MJup detected by transits, and it is one of the more massive and rapidly rotating planet hosts seen to date. We set out to derive a complete chemical pattern for this star, in order to compare it with those of chemically peculiar stars. Methods. We carried out a detailed abundance determination in the planet host star KELT-17 via spectral synthesis. Stellar parameters were estimated iteratively by fitting Balmer line profiles and imposing the Fe ionization balance using the SYNTHE program together with plane-parallel ATLAS12 model atmospheres. Specific opacities for an arbitrary composition and microturbulence velocity vmicro were calculated through the opacity sampling (OS) method. The abundances were determined iteratively by fitting synthetic spectra to metallic lines of 16 different chemical species using SYNTHE. The complete chemical pattern of KELT-17 was compared to the recently published average pattern of Am stars. We estimated the stellar radius using two methods: a) comparing the synthetic spectral energy distribution with the available photometric data and the Gaia parallax, and b) using a Bayesian estimation of stellar parameters using stellar isochrones. Results. We found over-abundances of Ti, Cr, Mn, Fe, Ni, Zn, Sr, Y, Zr, and Ba, together with subsolar values of Ca and Sc. Notably, the chemical pattern agrees with those recently published for Am stars, making KELT-17 the first exoplanet host whose complete chemical pattern is unambiguously identified with this class. The stellar radius derived by two different methods agrees to each other and with those previously obtained in the literature.
Diffusion Equations on Graphs

In brain imaging, the image acquisition and processing processes themselves are likely to introduce noise to the images. It is therefore imperative to reduce the noise while preserving the geometric details of the anatomical structures for various applications. Traditionally Gaussian kernel smoothing has been often used in brain image processing and analysis. However, the direct application of Gaussian kernel smoothing tend to cause various numerical issues in irregular domains with boundaries. For example, if one uses large bandwidth in kernel smoothing in a cortical bounded region, the smoothing will blur signals across boundaries. So in kernel smoothing and regression literature, various ad-hoc procedures were introduce to remedy the boundary effect. Diffusion equations have been widely used in brain imaging as a form of noise reduction. The most natural straightforward way to smooth images in irregular domains with boundaries is to formulate the problem as boundary value problems using partial differential equations. Numerous diffusion-based techniques have been developed in image processing. In this paper, we will overview the basics of isotropic diffusion equations and explain how to solve them on regular grids and irregular grids such as graphs.