Miracle-dz/UniFAR-FTUs huggingface dataset:
An Assessment of the Applicability of Three Reanalysis Snow Density Datasets Over China Using Ground Observations

Snow density is an important variable in snowpack research. The comprehensive applicability evaluation of the snow density datasets is a prerequisite of these datasets for their applications in hydrology processes and climate change, as well as in snow equivalent water retrieval algorithms. In this letter, the applicability of three snow density datasets, including European ReAnalysis (ERA)-Interim, ERA5, and the newly released ERA5-Land datasets, was first assessed using two ground evaluation datasets with different land covers from seven snow survey courses and four densely sampled networks in China. The results show that the ERA-Interim dataset significantly overestimates snow density during the entire snow season, with an overall root mean square error (RMSE) larger than 112 kg/m3, and lacks temporal dynamics. The ERA5 and ERA5-Land datasets are generally in good agreement with the ground measurements in China. The averaged RMSEs of the ERA5 dataset are 56.2 kg/m3 against snow course sites and 28.3 kg/m3 versus the densely sampled measurements, and those of the ERA5-Land dataset are 56.6 and 28.4 kg/m3, respectively. However, the ERA5 and ERA5-Land datasets still underestimate snow density over time, especially for the middle and late snow seasons. These new findings are expected to provide valuable feedback to model developers to further enhance the accuracy of snow density datasets.
A Review on News-Content Based Fake News Detection Approaches

Online Social Networks (OSN) have become very popular nowadays because of the numerous benefits they offer like helping users to get connected with friends, providing an ideal platform to share their opinions and thoughts, etc. But it is also seen that OSNs are a free channel for easy dissemination of fake news resulting in drastic and adverse effects in political, economic, and perhaps all aspects of society. Hence, automated detection of fake news becomes a dominant task. In this survey, we look at the state-of-the-art techniques in fake news detection, especially those based on news content and style. Many models equipped for fake news detection are examined and their performances compared. Human psychology tends to believe and hence forwards an article when it contains some appealing videos or audio along with the text. An article indeed gains more reach among users when it contains such multimedia content as images or videos rather than mere text. Fake news creators often take this idea as a dominance which prompts them to design attractive fake news to enhance the overall transparency of the news. Thus, we conclude that for fake news detection multimodal methods are very effective and it is necessary to explore unsupervised and semi-supervised learning algorithms for detection.
Enabling Low-power Radiometers with Machine Learning Calibration

Smart sensors of the future will be designed to extract maximum value information while minimizing the resources required to acquire, downlink and process data. Such intelligent instruments will reduce mission costs by avoiding taking measurements of uninteresting or unnecessary features while still capturing useful and beneficial data. However, many sensors like radiometers can make calibrated measurements only after reaching near thermal equilibrium; this requirement leads to wasted power, excess data, and delays in acquiring useful information. Furthermore, available spacecraft power and/or thermal requirements can lead to the need for power cycling a radiometer. Turning power off to an instrument stops its data acquisition but also leads to a loss of data when the instrument is powered back on until its electronics are sufficiently stable to make a calibrated measurement. As minimizing resource utilization is key to realizing future radiometer technology, in this paper, we propose the creation of a framework that uses machine learning techniques to unlock transient states of radiometers for obtaining calibrated measurements.
Solar wind electron halo depletions at 90° pitch angle

Strong depletions in solar wind electron halo distribution functions, centered on and roughly symmetric about 90° pitch angle, are present in at least 10% of data obtained by the Los Alamos SWEPAM experiment on the ACE spacecraft. Sunward‐directed conics are also present in the electron data, occur in conjunction with the 90° depletions, but are less common than depletions. We suggest that on open field lines both depletions and conics arise from focusing and mirroring effects associated with field line connection to magnetic field enhancements farther out in the heliosphere. The focusing and mirroring produces field‐aligned, counterstreaming fluxes of suprathermal electrons somewhat similar to that observed on closed field lines. Our observations provide strong evidence that the antisunward‐directed portion of the electron halo on open field lines at times results primarily from mirroring of back scattered strahl and/or shock‐heated electrons from far out in the heliosphere.
A Multi-Model Intelligent Approach for Rumor Detection in Social Networks

The impact of social media on public opinion has far-reaching repercussions across society. Even though social media give outlets to share news and opinions, the sheer number of posts on Twitter and Facebook makes it challenging to maintain quality control. These platforms have many users and offer various services, such as content creation and distribution. Not all information disseminated via the internet is accurate and reliable. Many people try to spread false and misleading information to influence public opinion. This paper reviews different algorithms for rumor identification, particularly fake news detection. It also proposes detection and classifications of fake news and its corresponding classification. Misinformation, commonly known as rumors, can cause serious harm due to unverified information. Despite their widespread use, the uncontrollable nature of social media platforms frequently results in the spread of rumors. One of the most sought after study areas in social media analytics is automatically recognizing rumors from tweets and posts.
Risk Assessment Tools on Trial: AI Systems Go?

In the 2015 Canadian case of Ewert v. Canada, risk assessment tools were put on trial in Canada’s Federal Court and eventually at the Supreme Court of Canada, their efficacy was challenged, and their reliability was upended <xref ref-type="bibr" rid="ref1">[1]</xref>, <xref ref-type="bibr" rid="ref2">[2]</xref>. Risk assessment tools are used by the justice system to present a calculated prediction of an offender’s risk of future criminal behavior. These tools are viewed by the legal community as precursors to machine learning and technological advancements in artificial intelligence (AI) in the criminal justice system, using algorithmic and data-driven decision-making to provide courts with predictions regarding an offender’s risk if released.
An Integrated Framework for Fault Resolution in Business Processes

Cloud and edge-computing based platforms have enabled rapid development of distributed business process (BP) applications in a plug and play manner. However, these platforms do not provide the needed capabilities for identifying or repairing faults in BPs. Faults in BP may occur due to errors made by BP designers because of their lack of understanding of the underlying component services, misconfiguration of these services, or incorrect/incomplete BP workflow specifications. Such faults may not be discovered at design or development stage and may occur at runtime. In this paper, we present a unified framework for automated fault resolution in BPs. The proposed framework employs a novel and efficient fault resolution approach that extends the generate-and-validate program repair approach. In addition, we propose a hybrid approach that performs fault resolution by analyzing a faulty BP in isolation as well as by comparing with other BPs using similar services. This hybrid approach results in improved accuracy and broader coverage of fault types. We also perform an extensive experimental evaluation to compare the effectiveness of the proposed approach using a dataset of 208 faulty BPs.
A Review On Physiological Attributes Based Biometric Authentication

This study involves the review of various types of physiological biometric authentication systems. Today, security is the most important concern. Biometric authentication technologies are becoming more popular as a method of verifying people’s identities. Biometric systems also add a layer of convenience for the user. They make it unnecessary for the user to “remember” numerous passwords for various apps, for example. A biometric system that uses a single biometric trait to identify persons must deal with issues such as spoof attacks, trait non-universality, substantial intra-class variability, limited degrees of freedom, and noisy data. Multi-modal biometrics, on the other hand, can be quite useful in this case because they do not have the same set of issues as unimodal biometrics. The performance of multimodal biometric systems, as they are known, has improved dramatically.
Iterative Decision-Directed Diversity Combining in Asymmetrically Clipped Optical OFDM

In this paper, a new signal demodulation technique is studied for diversity combined asymmetrically clipped optical orthogonal frequency-division multiplexing (ACO-OFDM). This technique is based on iterative decision-directed signal processing which can accurately estimate the sign information of the original unclipped signal and consequently lead to much higher performance gains compared to both the conventional receiver and the existing diversity combining receiver. The performance of this method is demonstrated using both Monte-Carlo simulations and a visible light communication (VLC) experiment.
CHAPTER 24: From Audio to Music Notation

The field of Music Information Retrieval (MIR) focuses on creating methods and practices for making sense of music data from various modalities, including audio, video, images, scores, and metadata [54]. Within MIR, a core problem which to the day remains open is Automatic Music Transcription (AMT), the process of automatically converting an acoustic music signal into some form of musical notation. The creation of a method for automatically converting musical audio to notation has several uses including but also going beyond MIR: from software for automatic typesetting of audio into staff notation or other music representations, to the use of automatic transcriptions as a descriptor towards the development of systems for music recommendation, to applications for interactive music systems such as automatic music accompaniment, for music education through methods for automatic instrument tutoring, and towards enabling musicological research in sound archives, to name but a few. Interest in AMT has grown during recent years as part of recent advances in artificial intelligence and in particular deep learning, which have led to new applications, systems, as well as have led to a new set of technical, methodological and ethical challenges related to this problem. This chapter presents state-of-the-art research and open topics in AMT, focusing on recent methods for addressing this task based on deep learning, as well as on outlining challenges and directions for future research. The first attempts to address this problem come back to the 1970s and the dawn of the field of computer music (e.g., [47]), while the problem faced a resurgence in the mid-2000s with the development of methods for audio signal processing and pattern recognition, and encountered a second wave of popularity in recent years following the emergence of deep learning methods. Irrespective of the methodologies used to investigate and develop tools and practices for AMT, researchers addressing this task draw knowledge from several disciplines, including digital signal processing,
Title : Control of an industrial copper solvent extraction process Year : 2009 Version : Post print

Rights: © 2009 Elsevier BV. This is the post print version of the following article: Komulainen, Tiina & Doyle III, Francis J. & Rantala, Ari & Jämsä-Jounela, Sirkka-Liisa. 2009. Control of an industrial copper solvent extraction process. Journal of Process Control. Volume 19, Issue 1. 19. ISSN 0959-1524 (printed). DOI: 10.1016/j.jprocont. 2008.04. 019, which has been published in final form at http://www.sciencedirect. com/science/article/pii/S0959152408000620.
Sexual Orientation Experiences of Discrimination: Dimensionality, internal consistency, and nomological validity in a diverse Colombian population Discrimination

Y. D. Gallo-Barrera contributed to the study conception and design data interpretation and statistical analysis, drafted the article, and revised and approved the final version. A. Campo-Arias contributed to the study design, data interpretation statistical analysis, revised the intellectual content, and approved the final version. Data availability statement : The data supporting this study's findings are available from the corresponding author upon reasonable request. Abstract The study aimed to determine the dimensionality, internal consistency, and nomological validity of the Sexual Orientation Experiences of Discrimination (SOEOD-9) among lesbian, gay, bisexual, and queer people in Santa Marta, Colombia. The study included 303 individuals between 18 and 70 years old. Participants completed the SOEOD-9. Dimensionality was explored using confirmatory (CFA) factor analysis; internal consistency with Cronbach's alpha and McDonald's omega; and nomological validity with Student’s test. The SOEOD-9 presented two factors that explained 51.6% of the variance and high internal consistency. Nevertheless, the CFA showed poor indicators of goodness-of-fit for the two-dimensional solution. A five-item version (SOEOD-5) improved overall performance (dimensionality, internal consistency, and nomological validity). In conclusion, the SOEOD-9 shows a two-factor internal structure with poor goodness-of-fit indicators and acceptable internal consistency and nomological validity. The SOEOD-5 presents better global indicators of dimensionality, internal consistency, and nomological validity.
Plane-to-Plane Positioning by Proximity-based Control

In this paper, we consider a multi-sensor arrangement of proximity sensors that forms a proximity array. A general modeling methodology is considered within the framework of Sensor-based Control. It incorporates multiple sensor signals from the proximity array by giving primary emphasis on the interaction screw. To prove its effectiveness, modeling approach is applied to the task of plane-to-plane positioning. We discuss the development of two sensor-based task functions for the specific task considered. The validity of the methodology is provided using relevant experimental results.
Uncertainty-CAM: Visual Explanation using Uncertainty based Class Activation Maps

—Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also pro- vide visual attention maps. Particularly, we solve for visual question answering task. We incorporate modern probabilistic deep learning methods that we further improve by using the gradients for these estimates. These have two-fold beneﬁts: a) improvement in obtaining the certainty estimates that correlate better with misclassiﬁed samples and b) improved attention maps that provide state-of-the-art results in terms of correlation with human attention regions. The improved attention maps result in consistent improvement for various methods for visual question answering. Therefore, the proposed technique can be thought of as a recipe for obtaining improved certainty estimates and explanations for deep learning models. We provide detailed empirical analysis for the visual question answering task on all standard benchmarks and comparison with state of the art methods.
PRBM_A_382999 2573..2586

Background: Body image dissatisfaction and aggressive behavior have become important public health problem in children and adolescents, and body image dissatisfaction may increase the occurrence of aggressive behavior. The aim of this study was to explore the correlation between body image dissatisfaction and aggressive behavior among Chinese children in different developmental stages. Methods: The stratified cluster sampling method was used to effectively survey 518 children aged 8–15 years. The Body Shape Questionnaire and the Buss-Warren Aggressive Questionnaire scale were used to measure body image dissatisfaction and aggressive behavior. Pubertal development stages were divided into three stages according to Tanner criteria. Results: There was a main correlation path of body image dissatisfaction hostility anger indirect aggression physical aggression in boys with stage I and stage II and in girls with stage I and stage III. In addition, there were direct paths of hostility indirect aggression, hostility verbal aggression, anger physical aggression, and anger verbal aggression in boys with stage I; hostility indirect aggression, hostility verbal aggression, and anger physical aggression in boys with stage II and in girls with stage I; and anger physical aggression, and anger verbal aggression in girls with stage III. Conclusion: Body image dissatisfaction might positively correlate with aggression through hostility among Chinese children and adolescents, and their association paths were different in different puberty stages.
The Extended η - F Composite Fading Distribution

—In this paper, the extended η - F composite fading distribution isproposed.Thisnewdistribution issimple,characterizedintermsofphys-ical parameters, able to model environments where the multipath fading coexists with shadowing and takes into account the clustering imbalance between the in-phase and quadrature components. For the aforementioned distribution, new expressions are derived for the probability density function (PDF); cumulative distribution function, higher-order moments and moment generating function of the instantaneous signal-to-noise ratio; joint envelope-phase PDF and amount of fading. Subsequently, expressions are deduced for the outage probability, average symbol error probability and average channel capacity. An asymptotic analysis is also provided. Severalcurvesarepresentedandthetheoreticalresultsarecorroboratedby Monte-Carlo simulations. Furthermore, an empirical validation is demon- strated in an underwater acoustic scenario, in which a better ﬁt between the theoretical PDF and the empirical data is perceived for the extended η - F model, when compared to the extended η - μ and κ - μ shadowed distributions.
A Modeling and Analysis Study Reveals That CaMKII in Synaptic Plasticity Is a Dominant Affecter in CaM Systems in a T286 Phosphorylation-Dependent Manner

NMDAR-dependent synaptic plasticity in the hippocampus consists of two opposing forces: long-term potentiation (LTP), which strengthens synapses and long-term depression (LTD), which weakens synapses. LTP and LTD are associated with memory formation and loss, respectively. Synaptic plasticity is controlled at a molecular level by Ca2+-mediated protein signaling. Here, Ca2+ binds the protein, calmodulin (CaM), which modulates synaptic plasticity in both directions. This is because Ca2+-bound CaM activates both LTD-and LTP-inducing proteins. Understanding how CaM responds to Ca2+ signaling and how this translates into synaptic plasticity is therefore important to understanding synaptic plasticity induction. In this paper, CaM activation by Ca2+ and calmodulin binding to downstream proteins was mathematically modeled using differential equations. Simulations were monitored with and without theoretical knockouts and, global sensitivity analyses were performed to determine how Ca2+/CaM signaling occurred at various Ca2+ signals when CaM levels were limiting. At elevated stimulations, the total CaM pool rapidly bound to its protein binding targets which regulate both LTP and LTD. This was followed by CaM becoming redistributed from low-affinity to high-affinity binding targets. Specifically, CaM was redistributed away from LTD-inducing proteins to bind the high-affinity LTP-inducing protein, calmodulin-dependent kinase II (CaMKII). In this way, CaMKII acted as a dominant affecter and repressed activation of opposing CaM-binding protein targets. The model thereby showed a novel form of CaM signaling by which the two opposing pathways crosstalk indirectly. The model also found that CaMKII can repress cAMP production by repressing CaM-regulated proteins, which catalyze cAMP production. The model also found that at low Ca2+ stimulation levels, typical of LTD induction, CaM signaling was unstable and is therefore unlikely to alone be enough to induce synaptic depression. Overall, this paper demonstrates how limiting levels of CaM may be a fundamental aspect of Ca2+ regulated signaling which allows crosstalk among proteins without requiring directly interaction.
Dark Energy Phenomenology

We discuss the phenomenology of the dark energy in first order perturbation theory, demonstrating that the dark energy cannot be fully constrained unless the dark matter is found, and that there are two functions that characterise the observational properties of the dark sector for cosmological probes. We argue that measuring these two functions should be an important goal for observational cosmology in the next decades.
Organization assessment through McKinsey 7 S, Balanced
Scorecard and SWOT metrics

The purpose of this paper is two-fold: to assess an organization, Bricks Inc. (name anonymized for confidential reasons) with the help of McKinsey 7 S, Balanced Scorecard and SWOT metrics, and to make a comparative perspective on the current and proposed changes in the management structure with the aid of organizational charts. The study shows thatwhile Bricks Inc. has been successful in making profits over the years, there are a number of challenges which need to be encountered. Bricks Inc. operates across the globe and has a number of divisions. Further on, the company has been running its operations in different sectors like real estate, hospitality, retail, investments, etc. The leaves recommendations that Bricks Inc. should aim at consolidation of its divisions and aim at a proper analysis of political and legal environment of the country in which it seeks to operate to avoid any legal complications. Finally, the study recommends that there are a number of areas which may be ventured into but a proper strategy will be required to implement its plans.