Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of Yang et al. “CARDAMOM-FluxVal Version 1.0: a FLUXNET-based Validation System for CARDAMOM Carbon and Water Flux Estimates” Reviewer 1 General Comments

1.1 Yang et al present a consistent validation framework (including a suite a model evaluation metrics) for a terrestrial biosphere model data assimilation system (CARDAMOM) that can be used to systematically test different versions of the model in addition to various DA system configurations (observation record length etc). The introduction really nicely lays out the motivation for developing a rigorous DA-validation framework and the analyses presented provide a useful demonstration of how that framework can be used to answer the key questions posed in lines 90-95. This is important work that all TBM DA groups need to be routinely performing, and as such this paper should serve as a guiding framwork or benchmark that I believe will be of wide use to the TBM modeling community.
PERSONALITY TRAITS AND FACEBOOK ADDICTION AMONG TEENAGERS AND YOUNG ADULTS

The present study aimed to understand which of the big five personality traits of the teenagers and young adults have tendencies to lead the commonly Facebook usage toward Facebook addiction. Very few extensive researches have been conducted on this subject in Pakistan. The study aimed to identify the relationship of some particular Big Five Personality traits as well as predicting those traits with Facebook addiction. This study has also evaluated the differences of Facebook addiction in terms of age between teenager and young adult students. An initial purposive sample of 300 Facebook users including 150 teenager students (75 girls, 75 boys) and 150 young adult students (75 girls, 75 boys) was recruited from various schools and colleges. From this sample of 300 Facebook users, 86 participants were indicated as Facebook addicts as assessed by the Bergen Facebook Addiction Scale. The Big Five Markers Personality Scale and Bergen Facebook Addiction Scale were used as assessment measures. Pearson product moment correlation, linear regression and independent sample t-test were used to analyze the data. The results indicated significant positive relationship and predict of Extraversion while significant negative relationship and predict of Conscientiousness traits with Facebook addiction. Agreeableness, Emotional Stability and Intellect personality traits were insignificant for Facebook addiction. Additionally, insignificant differences were revealed between Facebook addicted teenagers and young adults. The study would be an addition to the academic literature on personality and social psychology with various implications for better understanding of Facebook addicted teenagers and young adults including counseling and psychotherapy as well.
NOT FOR CIRCULATION

Introduction Insect vector-borne protozoan parasites of the order Kinetoplastida cause a range of neglected human diseases, most notably leishmaniasis, Chagas disease and African sleeping sickness. Globally, these diseases lead to greater than 100,000 deaths annually, and cause the loss of more than 4 million disability-adjusted life years (ie, healthy years of life lost as a result of premature death and disability) [1]. The mortality and morbidity caused by leishmaniasis (both visceral leishmaniasis [VL] and cutaneous leishmaniasis [CL]) is only surpassed among parasitic diseases by malaria and lymphatic filariasis [2]. The kinetoplastid parasites that cause leishmaniasis are endemic in tropical and subtropical regions and, therefore, disproportionately affect the health and economic viability of most of the developing world. Leishmania spp – the causative agents of leishmaniasis – infect more than 12 million individuals in five continents, and are endemic in 88 countries; thus, more than 350 million individuals are at risk of infection [1]. Instances of leishmaniasis are also not uncommon in certain regions of North America and southern Europe. In addition, the spread and severity of the disease is exacerbated by its status as a possible coinfection in patients with AIDS and the overlap in prevalence of HIV and Leishmania [3].
Parents’ Involvement in Distance Learning During the Covid-19 Pandemic in the South Region of Saudi Arabia

During 2020, in response to the COVID-19 global pandemic, the majority of schools, including those in the Kingdom of Saudi Arabia (KSA), temporarily closed their doors. This led to a rapid shift towards distance learning, placing considerable responsibility on parents and guardians. The current study examines the experience of parents, employing a questionnaire distributed through the social media platform WhatsApp. The research used purposive sampling with parents with children aged between three and six. The study reveals the challenges experienced by parents during the pandemic, including concerns for their children’s behavioral, emotional, social and cognitive development, and isolation from peers. Parents also emphasized the importance of blended education, i.e. combining school-based and distance education. In addition, this study supports Sui-Chu and Willms’ (1996) conclusion that parents of a higher socioeconomic status tend to become more involved in their children's education. This research offers a number of recommendations, including combining distance education with school-based learning to strengthen the communication skills of students and enhance behavioral and social development, as well as to provide training courses for parents and students on the use of various educational platforms, i.e. Zoom.
Use of Bentonite and Zeolite to Stabilize Nutrients and Heavy Metals Within Earthen Pond Sediments

Leaving out the sediments of earthen ponds for several years without disinfection or treatment leads to high concentrations of nitrogen and phosphate compounds, heavy metals and increasing sediment pollutants which release into the water. Therefore, this study aimed to evaluate the effectiveness of capping or mixing of bentonite and zeolite in different percentages for the stabilizing of nutrients and heavy metals in earthen pond sediments for interrupt their release to the water. The pH and electrical conductivity (EC), NH4-N, NO3-N, PO4P and heavy metals (Cd, Cu, Fe, Mn, Ni, Pb and Zn) in the sediments were measured by 45-day laboratory incubation experiments. The results showed that the highest stabilization efficiency of NH4-N, NO3-N, Cd, Cu, Pb and Zn in the sediments was recorded with zeolite capping of 6% of the sediment weight (ZC 6%) by 65.4%, 41.8%, 66.0 %, 63.9 %, 54.2 % and 57.3 %, respectively, while, the highest stabilization efficiency of PO4-P, Fe, Mn and Ni was with bentonite capping of 6% of the sediment weight (BC 6%) by 61.9%, 42.7 %, 57.0 % and 52.6 %, respectively. Therefore, it is recommended to use bentonite or zeolite as capping material for sediment at a rate of 6% at the bottom of the pond can be effective for the control of nutrients and heavy metals when they are increased in earthen pond sediments for interrupt their release into water. keywords: Bentonite Zeolite Nutrients Heavy Metals Sediments Earthen Pond.
Occupational Gender stereotypes in Indic Languages

As the use of Natural Language Processing systems increases, there is a need to address the gender bias in these systems. While research is being done in the English language to quantify and eliminate bias, it is not the same for Indic Languages. Most Indic languages are gendered, i.e., each noun is assigned a gender. Therefore, evaluation differs from what is done in English. In this paper, we evaluate and find occupational bias in Indic languages. We prepare a dataset of gender neutral occupations and gendered occupation pairs, in Hindi and Marathi.
The impact of COVID-19 non-pharmaceutical interventions on future respiratory syncytial virus transmission in South Africa

In response to the COVID-19 pandemic, the South African government employed various nonpharmaceutical interventions (NPIs) in order to reduce the spread of SARS-CoV-2. In addition to mitigating transmission of SARS-CoV-2, these public health measures have also functioned in slowing the spread of other endemic respiratory pathogens. Surveillance data from South Africa indicates low circulation of respiratory syncytial virus (RSV) throughout the 2020-2021 Southern Hemisphere winter seasons. Here we fit age-structured epidemiological models to national surveillance data to predict the 2022 RSV outbreak following two suppressed seasons. We project a 32% increase in the peak number of monthly hospitalizations among infants < 2 years, with older infants (6-23 month olds) experiencing a larger portion of severe disease burden than typical. Our results suggest that hospital system readiness should be prepared for an intense RSV season in early 2022.
End-to-End Learning to Grasp from Object Point Clouds

—The ability to grasp objects is an essential skill that enables many robotic manipulation tasks. Recent works have studied point cloud-based methods for object grasping by starting from simulated datasets and have shown promising performance in real-world scenarios. Nevertheless, many of them still strongly rely on ad-hoc geometric heuristics to generate grasp candidates, which fail to generalize to objects with signiﬁcantly different shapes with respect to those observed during training. Moreover, these methods are generally inefﬁcient with respect to the number of training samples and the time needed during deployment. In this paper, we propose an end-to-end learning solution to generate 6-DOF parallel-jaw grasps starting from the partial view of the object. Our Learning to Grasp (L2G) method takes as input object point clouds and is guided by a principled multi-task optimization objective that generates a diverse set of grasps combining contact point sampling, grasp regression, and grasp evaluation. With a thorough experimental analysis, we show the effectiveness of the proposed method as well as its robustness and generalization abilities.
Koopman Methods for Estimation of Animal Motions over Unknown, Regularly Embedded Submanifolds

— This paper introduces a data-dependent approximation of the forward kinematics map for certain types of animal motion models. It is assumed that motions are supported on a low-dimensional, unknown conﬁguration manifold Q that is regularly embedded in high dimensional Euclidean space X := R d . This paper introduces a method to estimate forward kinematics from the unknown conﬁguration submanifold Q to an n -dimensional Euclidean space Y := R n of observations. A known reproducing kernel Hilbert space (RKHS) is deﬁned over the ambient space X in terms of a known kernel function, and computations are performed using the known kernel deﬁned on the ambient space X . Estimates are constructed using a certain data-dependent approximation of the Koopman operator deﬁned in terms of the known kernel on X . However, the rate of convergence of approximations is studied in the space of restrictions to the unknown manifold Q . Strong rates of convergence are derived in terms of the ﬁll distance of samples in the unknown conﬁguration manifold, provided that a novel regularity result holds for the Koopman operator. Additionally, we show that the derived rates of convergence can be applied in some cases to estimates generated by the extended dynamic mode decomposition (EDMD) method. We illustrate characteristics of the estimates for simulated data as well as samples collected during motion capture experiments.
LINNA: Likelihood Inference Neural Network Accelerator

Bayesian posterior inference of modern multi-probe cosmological analyses incurs massive computational costs. For instance, depending on the combinations of probes, a single posterior inference for the Dark Energy Survey (DES) data had a wall-clock time that ranged from 1 to 21 days using a state-of-the-art computing cluster with 100 cores. These computational costs have severe environmental impacts and the long wall-clock time slows scientific productivity. To address these difficulties, we introduce LINNA: the Likelihood Inference Neural Network Accelerator. Relative to the baseline DES analyses, LINNA reduces the computational cost associated with posterior inference by a factor of 8–50. If applied to the first-year cosmological analysis of Rubin Observatory's Legacy Survey of Space and Time (LSST Y1), we conservatively estimate that LINNA will save more than U.S. $300,000 on energy costs, while simultaneously reducing CO2 emission by 2,400 tons. To accomplish these reductions, LINNA automatically builds training data sets, creates neural network emulators, and produces a Markov chain that samples the posterior. We explicitly verify that LINNA accurately reproduces the first-year DES (DES Y1) cosmological constraints derived from a variety of different data vectors with our default code settings, without needing to retune the algorithm every time. Further, we find that LINNA is sufficient for enabling accurate and efficient sampling for LSST Y10 multi-probe analyses. We make LINNA publicly available at https://github.com/chto/linna, to enable others to perform fast and accurate posterior inference in contemporary cosmological analyses.
LFW-Beautified: A Dataset of Face Images with Beautification and Augmented Reality Filters

Selfie images enjoy huge popularity in social media. The same platforms centered around sharing this type of images offer filters to beautify them or incorporate augmented reality effects. Studies suggests that filtered images attract more views and engagement. Selfie images are also in increasing use in security applications due to mobiles becoming data hubs for many transactions. Also, video conference applications, boomed during the pandemic, include such filters. Such filters may destroy biometric features that would allow person recognition or even detection of the face itself, even if such commodity applications are not necessarily used to compromise facial systems. This could also affect subsequent investigations like crimes in social media, where automatic analysis is usually necessary given the amount of information posted in social sites or stored in devices or cloud repositories. To help in counteracting such issues, we contribute with a database of facial images that includes several manipulations. It includes image enhancement filters (which mostly modify contrast and lightning) and augmented reality filters that incorporate items like animal noses or glasses. Additionally, images with sunglasses are processed with a reconstruction network trained to learn to reverse such modifications. This is because obfuscating the eye region has been observed in the literature to have the highest impact on the accuracy of face detection or recognition. We start from the popular Labeled Faces in the Wild (LFW) database, to which we apply different modifications, generating 8 datasets. Each dataset contains 4,324 images of size 64 x 64, with a total of 34,592 images. The use of a public and widely employed face dataset allows for replication and comparison. The created database is available at https://github.com/HalmstadUniversityBiometrics/LFW-Beautified
Deep Learning for Wireless Dynamics

This paper aims to predict radio channel variations over time by deep learning from channel observations without knowledge of the underlying channel dynamics. In next generation wideband cellular systems, multicarrier transmission for higher data rate leads to high-resolution predicting problem. By leveraging recent advances of deep learning in high-resolution image processing, we propose a purely data-driven deep learning (DL) approach to predicting high-resolution temporal evolution of wideband radio channels. In order to investigate the effect of architectural design choices, we develop and study three deep learning prediction models, namely, baseline, image completion and next-frame prediction models using UNet. Numerical results show that the proposed DL approach achieves a 52% lower prediction error than the traditional approach based on Kalman filter (KF) in mean absolute errors. To quantify impact of channel aging and prediction on precoding performance, we also evaluate the performance degradation due to outdated and predicted channel state information (CSI) compared to perfect CSI. Our simulations show that the proposed DL approach can reduce the performance loss due to channel aging by 71 % through adapting precoding vector to changes in radio channel while the traditional KF approach only shows a 27% reduction.
Hyperbolic Image Segmentation

For image segmentation, the current standard is to perform pixel-level optimization and inference in Euclidean output embedding spaces through linear hyperplanes. In this work, we show that hyperbolic manifolds provide a valuable alternative for image segmentation and propose a tractable formulation of hierarchical pixel-level classification in hyperbolic space. Hyperbolic Image Segmentation opens up new possibilities and practical benefits for segmentation, such as uncertainty estimation and boundary information for free, zero-label generalization, and increased performance in low-dimensional output embeddings.
Semiclassical spectral gaps of the 3D Neumann Laplacian with constant magnetic field

This article deals with the spectral analysis of the semiclassical Neumann magnetic Laplacian on a smooth bounded domain in dimension three. When the magnetic field is constant and in the semiclassical limit, we establish a four-term asymptotic expansion of the low-lying eigenvalues, involving a geometric quantity along the apparent contour of Ω in the direction of the field. In particular, we prove that they are simple.
The Competent Computational Thinking Test: Development and Validation of an Unplugged Computational Thinking Test for Upper Primary School

With the increasing importance of Computational Thinking (CT) at all levels of education, it is essential to have valid and reliable assessments. Currently, there is a lack of such assessments in upper primary school. That is why we present the development and validation of the competent CT test (cCTt), an unplugged CT test targeting 7–9 year-old students. In the first phase, 37 experts evaluated the validity of the cCTt through a survey and focus group. In the second phase, the test was administered to 1519 students. We employed Classical Test Theory, Item Response Theory, and Confirmatory Factor Analysis to assess the instruments’ psychometric properties. The expert evaluation indicates that the cCTt shows good face, construct, and content validity. Furthermore, the psychometric analysis of the student data demonstrates adequate reliability, difficulty, and discriminability for the target age groups. Finally, shortened variants of the test are established through Confirmatory Factor Analysis. To conclude, the proposed cCTt is a valid and reliable instrument, for use by researchers and educators alike, which expands the portfolio of validated CT assessments across compulsory education. Future assessments looking at capturing CT in a more exhaustive manner might consider combining the cCTt with other forms of assessments.
Opportunities for new physics searches with heavy ions at colliders

Opportunities for searches for phenomena beyond the Standard Model (BSM) using heavy-ions beams at high energies are outlined. Different BSM searches proposed in the last years in collisions of heavy ions, mostly at the Large Hadron Collider, are summarized. A few concrete selected cases are reviewed including searches for axion-like particles, anomalous $\tau$ electromagnetic moments, magnetic monopoles, and dark photons. Expectations for the achievable sensitivities of these searches in the coming years are given. Studies of CP violation in hot and dense QCD matter and connections to ultrahigh-energy cosmic rays physics are also mentioned.
MS2M: A message-based approach for live stateful microservices migration

In the last few years, the proliferation of edge and cloud computing infrastructures as well as the increasing number of mobile devices has facilitated the emergence of many novel applications. However, that increase of complexities also creates novel challenges for service providers, for example, the efficient management of interdependent services during runtime. One strategy is to reallocate services dynamically by migrating them to suitable servers. However, not every microservice can be deployed as stateless instances, which leads to suboptimal performance of live migration techniques. In this work, we propose a novel live migration scheme focusing on stateful microservices in edge/cloud environments by utilizing the underlying messaging infrastructure to reconstruct the service's state. Not only can this approach be applied in various microservice deployment scenarios, experimental evaluation results also show a reduction of 19.92% downtime compared to the stop-and-copy migration method.
Some applications of TDA on ﬁnancial markets

The Topological Data Analysis (TDA) has had many applications. However, ﬁnancial markets has been studied slightly through TDA. Here we present a quick review of some recent applications of TDA on ﬁnancial markets and propose a new turbulence index based on persistent homology — the fundamental tool for TDA — that seems to capture critical transitions on ﬁnancial data, based on our experiment with SP500 data before 2020 stock market crash in February 20, 2020, due to the COVID-19 pan-demic. We review applications in the early detection of turbulence periods in ﬁnancial markets and how TDA can help to get new insights while investing and obtain superior risk-adjusted returns compared with investing strategies using classical turbulence indices as VIX and the Chow’s index based on the Mahalanobis distance. Furthermore, we include an introduction to persistent homology so the reader could be able to understand this paper without knowing TDA.
Flat Delaunay Complexes for Homeomorphic Manifold Reconstruction

Given a smooth submanifold of the Euclidean space, a finite point cloud and a scale parameter, we introduce a construction which we call the flat Delaunay complex (FDC). This is a variant of the tangential Delaunay complex (TDC) introduced by Boissonnat et al. Building on their work, we provide a short and direct proof that when the point cloud samples sufficiently nicely the submanifold and is sufficiently safe (a notion which we define in the paper), our construction is homeomorphic to the submanifold. Because the proof works even when data points are noisy, this allows us to propose a perturbation scheme that takes as input a point cloud sufficiently nice and returns a point cloud which in addition is sufficiently safe. Equally importantly, our construction provides the framework underlying a variational formulation of the reconstruction problem which we present in a companion paper.
New, Spherical Solutions of Non-Relativistic, Dissipative Hydrodynamics

We present a new family of exact solutions of dissipative fireball hydrodynamics for arbitrary bulk and shear viscosities. The main property of these solutions is a spherically symmetric, Hubble flow field. The motivation of this paper is mostly academic: we apply non-relativistic kinematics for simplicity and clarity. In this limiting case, the theory is particularly clear: the non-relativistic Navier–Stokes equations describe the dissipation in a well-understood manner. From the asymptotic analysis of our new exact solutions of dissipative fireball hydrodynamics, we can draw a surprising conclusion: this new class of exact solutions of non-relativistic dissipative hydrodynamics is asymptotically perfect.