Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding workplace bullying among staff in higher education

UNDERSTANDING WORKPLACE BULLYING AMONG STAFF IN HIGHER EDUCATION The purpose of this phenomenological study was to discover how staff members in a division of student affairs define workplace bullying, whether or not they have experienced workplace bullying, and finally whether or not they have witnessed workplace bullying in higher education. Participants were invited to complete an open-ended online survey where they could define workplace bullying and share their personal experiences with bullying or witnessing bullying in the workplace. The study explored the lived experiences of student affairs professionals with personal and bystander experiences of bullying in the higher education workplace. Social identity theory relating to in-group and out-group formation in the workplace was used to understand the experiences of participants. Data analysis provided details of participants’ definition of bullying in higher education in addition to their own personal and witnessed experiences. Participants defined, experienced, and witnessed bullying in the workplace the same as faculty and professionals outside of higher education. Participants’ experiences were similar to faculty and non-higher education professionals in all areas except for the reporting of bullying. Unlike faculty and non-higher education employees, student affairs staff members are more likely to report bullying experiences. Using the results and conclusions, several levels of employee training were recommended to be implemented in higher education to help prevent future bullying experiences of staff members.
Static Symmetry Breaking in Many-Sorted Finite Model Finding

Symmetry in finite model finding problems of many-sorted first-order logic (MSFOL) can be exploited to reduce the number of interpretations considered during search, thereby improving solver performance. In this thesis, we situate symmetry of many-sorted finite model finding (MSFMF) problems in a general framework used for constraint satisfaction problems (CSP). We survey and classify existing approaches to symmetry for MSFOL as used in tools such as Paradox. We provide new insight into how sorts affect the existence of symmetry and how sort inference can be viewed as a symmetry detection mechanism. Finally, we present two new symmetry breaking schemes for MSFOL that are implemented at the MSFOL level and discuss when schemes can be combined. We prove the correctness of our new methods.
Fast calculation of the variance of edge crossings in random linear arrangements

The interest in spatial networks where vertices are embedded in a one-dimensional space is growing. Remarkable examples of these networks are syntactic dependency trees and RNA structures. In this setup, the vertices of the network are arranged linearly and then edges may cross when drawn above the sequence of vertices. Recently, two aspects of the distribution of the number of crossings in uniformly random linear arrangements have been investigated: the expectation and the variance. While the computation of the expectation is straightforward, that of the variance is not. Here we present fast algorithms to calculate that variance in arbitrary graphs and forests. As for the latter, the algorithm calculates variance in linear time with respect to the number of vertices. This paves the way for many applications that rely on an exact but fast calculation of that variance. These algorithms are based on novel arithmetic expressions for the calculation of the variance that we develop from previous theoretical work.
HUBUNGAN KURANG ENERGI KRONIK DAN ANEMIA PADA IBU HAMIL DENGAN STATUS GIZI BAYI USIA 6-12 BULAN DI KABUPATEN BOYOLALI (CORRELATION CHRONIC ENERGY DEFICIENCY AND ANEMIA DURING PREGNANCY WITH NUTRITIONAL STATUS OF INFANT 6 – 12 MONTHS IN BOYOLALI REGENCY)

ABSTRACT Malnutrition still becomes a problem in developing countries such as Indonesia. In Boyolali regency the prevalence of stunting and wasting of infants was 27,5 percent and 6,6 percent respectively. The aim of this study was to analyze the correlation between chronic energy deficiency and Anemia on pregnant mothers with the nutritional status of infants aged 6-12 months. Prospective cohort design was implemented in authorized administration of 6 Community Health Centers of Boyolali Regency. A number of 40 pregnant mothers who had record of chronic energy deficiency and Anemia during third trimester and have infants aged 6-12 months participated in the study. Statistic analyzed were performed using the Peason’s Correlation method. The study failed to show the relationship between chronic energy deficiency during pregnancy and z-score of weight for age (WAZ), height for age (HAZ) and weight for height (WHZ) of infants p>0.05 while anemic pregnant mother found a correlation with Z-score of weight for height (WHZ) of infants (p 0.05) of infant 6-12 months. Thus, early improvement nutrition program for adolescence should be done to prevent malnutrition in pregnant mothers such as chronic energy deficiency and anemia. ABSTRAK Gangguan gizi masih merupakan masalah di negara berkembang termasuk Indonesia. Di Kabupaten Boyolali prevalensi balita pendek ( stunting ) sebesar 27,5 persen dan kurus (wasting) 6,6 persen. Penelitian ini bertujuan menganalisis hubungan riwayat kurang energi kronik (KEK) dan anemia pada ibu hamil dengan status gizi bayi usia 6-12 bulan. Desain Penelitian adalah kohort prospektif di enam wilayah Puskesmas, di Kabupaten Boyolali. Partisipan terdiri dari 40 ibu yang memiliki riwayat KEK dan anemia pada kehamilan trimester III, memiliki bayi berusia 6-12 bulan. Analisis statistik menggunakan uji korelasi pearson. Pada penelitian ini tidak ditemukan hubungan riwayat KEK pada ibu hamil dengan status gizi bayi usia 6-12 bulan (Indeks BB/U, PB/U dan BB/PB) (p>0,05). Riwayat anemia pada ibu hamil berhubungan dengan status gizi bayi indeks BB/PB (p 0,05). Perbaikan pencegahan kurang gizi dan anemia pada masa remaja atau calon pengantin diperlukan untuk mencegah terjadinya gizi kurang pada ibu hamil dan generasi selanjutnya.
A Systematic Study of Gamma-Ray Flares from the Crab Nebula with Fermi-LAT. I. Flare Detection

Significant flares of GeV γ-ray emission from the Crab Nebula were found by AGILE and Fermi-LAT years ago, indicating that extreme particle acceleration and radiation occurs in young pulsar wind nebulae. To enlarge the flare sample and to investigate their statistical properties will be very useful in understanding the nature of the γ-ray flares. In this paper, we investigate the flaring emission from the Crab Nebula with eleven year observations of the Fermi-LAT. We identify 17 significant flares in the light curve of the low-energy (synchrotron) component of the γ-ray emission. The flare rate is about 1.5 per year, without any significant change or clustering during the 11 years of the observation. We detect a special flare with an extremely long duration of nearly one month, that occurred in 2018 October, with synchrotron photons up to energies of about 1 GeV. The synchrotron component could be fitted by a steady power-law (PL) background and a variable flare component with an exponentially cutoff PL spectrum, not only for the individual flare but also for the combined data, which may favor a similar emission mechanism for all flares. However, we do not find a universal relation between the cutoff energy and the energy fluxes of the flares, which may reflect the complicated acceleration and/or cooling processes of the involved particles.
Bounds on the entanglement entropy by the number entropy in non-interacting fermionic systems

Entanglement in a pure state of a many-body system can be characterized by the Rényi entropies S^{(\alpha)}=\ln\textrm{tr}(\rho^\alpha)/(1-\alpha)S(α)=lntr(ρα)/(1−α) of the reduced density matrix \rhoρ of a subsystem. These entropies are, however, difficult to access experimentally and can typically be determined for small systems only. Here we show that for free fermionic systems in a Gaussian state and with particle number conservation, S^{(2)}S(2) can be tightly bound—from above and below—by the much easier accessible Rényi number entropy S^{(2)}_N=-\ln \sum_n p^2(n)SN(2)=−ln∑np2(n) which is a function of the probability distribution p(n)p(n) of the total particle number in the considered subsystem only. A dynamical growth in entanglement, in particular, is therefore always accompanied by a growth—albeit logarithmically slower—of the number entropy. We illustrate this relation by presenting numerical results for quenches in non-interacting one-dimensional lattice models including disorder-free, Anderson-localized, and critical systems with off-diagonal (bond) disorder.
Coupling local to global star formation in spiral galaxies: the effect of differential rotation

Star formation is one of the key factors that shapes galaxies. This process is relatively well understood from both simulations and observations on a small "local" scale of individual giant molecular clouds and also on a "global" galaxy-wide scale (e.g. the Kennicutt-Schmidt law). However, there is still no understanding on how to connect global to local star formation scales and whether this connection is at all possible. Here we analyze spatially resolved kinematics and the star formation rate density $\Sigma_{SFR}$ for a combined sample of 17 nearby spiral galaxies obtained using our own optical observations in H$\alpha$ for 9 galaxies and neutral hydrogen radio observations combined with a multi-wavelength spectral energy distribution analysis for 8 galaxies from the THINGS project. We show that the azimuthally averaged normalized star formation rate density in spiral galaxies on a scale of a few hundred parsecs is proportional to the kinetic energy of giant molecular cloud collisions due to differential rotation of the galactic disc. This energy is calculated from the rotation curve using the two Oort parameters A and B as $\log (\Sigma_{SFR} / SFR_{tot}) \propto \log[2 A^2+ 5 B^2]$. The total kinetic energy of collision is defined by the shear velocity that is proportional to A and the rotational energy of a cloud proportional to the vorticity B. Hence, shear does not act as a stabilizing factor for the cloud collapse thus reducing star formation but rather increases it by boosting the kinetic energy of collisions. This result can be a tool through which one can predict a radial distribution of star formation surface density using only a rotation curve.
Asynchronous and Parallel Distributed Pose Graph Optimization

We present Asynchronous Stochastic Parallel Pose Graph Optimization (<inline-formula><tex-math notation="LaTeX">$\textsc {ASAPP}$</tex-math></inline-formula>), the first <italic>asynchronous</italic> algorithm for distributed pose graph optimization (PGO) in multi-robot simultaneous localization and mapping. By enabling robots to optimize their local trajectory estimates without synchronization, <inline-formula><tex-math notation="LaTeX">$\textsc {ASAPP}$</tex-math></inline-formula> offers resiliency against communication delays and alleviates the need to wait for stragglers in the network. Furthermore, <inline-formula><tex-math notation="LaTeX">$\textsc {ASAPP}$</tex-math></inline-formula> can be applied on the rank-restricted relaxations of PGO, a crucial class of non-convex Riemannian optimization problems that underlies recent breakthroughs on globally optimal PGO. Under bounded delay, we establish the global first-order convergence of <inline-formula><tex-math notation="LaTeX">$\textsc {ASAPP}$</tex-math></inline-formula> using a sufficiently small stepsize. The derived stepsize depends on the worst-case delay and inherent problem sparsity, and furthermore matches known result for synchronous algorithms when there is no delay. Numerical evaluations on simulated and real-world datasets demonstrate favorable performance compared to state-of-the-art synchronous approach, and show <inline-formula><tex-math notation="LaTeX">$\textsc {ASAPP}$</tex-math></inline-formula>'s resilience against a wide range of delays in practice.
End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification

The most common approach to speaker diarization is clustering of speaker embeddings. However, the clustering-based approach has a number of problems; i.e., (i) it is not optimized to minimize diarization errors directly, (ii) it cannot handle speaker overlaps correctly, and (iii) it has trouble adapting their speaker embedding models to real audio recordings with speaker overlaps. To solve these problems, we propose the End-to-End Neural Diarization (EEND), in which a neural network directly outputs speaker diarization results given a multi-speaker recording. To realize such an end-to-end model, we formulate the speaker diarization problem as a multi-label classification problem and introduce a permutation-free objective function to directly minimize diarization errors. Besides its end-to-end simplicity, the EEND method can explicitly handle speaker overlaps during training and inference. Just by feeding multi-speaker recordings with corresponding speaker segment labels, our model can be easily adapted to real conversations. We evaluated our method on simulated speech mixtures and real conversation datasets. The results showed that the EEND method outperformed the state-of-the-art x-vector clustering-based method, while it correctly handled speaker overlaps. We explored the neural network architecture for the EEND method, and found that the self-attention-based neural network was the key to achieving excellent performance. In contrast to conditioning the network only on its previous and next hidden states, as is done using bidirectional long short-term memory (BLSTM), self-attention is directly conditioned on all the frames. By visualizing the attention weights, we show that self-attention captures global speaker characteristics in addition to local speech activity dynamics, making it especially suitable for dealing with the speaker diarization problem.
Towards a predictive spatio-temporal representation of brain data

The characterisation of the brain as a "connectome", in which the connections are represented by correlational values across timeseries and as summary measures derived from graph theory analyses, has been very popular in the last years. However, although this representation has advanced our understanding of the brain function, it may represent an oversimplified model. This is because the typical fMRI datasets are constituted by complex and highly heterogeneous timeseries that vary across space (i.e., location of brain regions). We compare various modelling techniques from deep learning and geometric deep learning to pave the way for future research in effectively leveraging the rich spatial and temporal domains of typical fMRI datasets, as well as of other similar datasets. As a proof-of-concept, we compare our approaches in the homogeneous and publicly available Human Connectome Project (HCP) dataset on a supervised binary classification task. We hope that our methodological advances relative to previous "connectomic" measures can ultimately be clinically and computationally relevant by leading to a more nuanced understanding of the brain dynamics in health and disease. Such understanding of the brain can fundamentally reduce the constant specialised clinical expertise in order to accurately understand brain variability.
Annual parallax measurement of the Mira variable star BX Camelopardalis with VERA

We report results of astrometric VLBI observations toward a Mira variable star BX~Cam using the VLBI array "VERA". The observations were performed from February 2012 to November 2014. Obtained parallax is 1.73$\pm$0.03 mas corresponding to a distance of 0.58$\pm$0.01 kpc. Parallax of this source was also reported in Gaia DR2 as 4.13$\pm$0.25 mas, and there is a 240 \% difference between these two measurements. Astrometric results from our VLBI observations show that we exactly traced angular motions of the seven maser spots in BX~Cam. We calculated stellar luminosities using both parallaxes, and obtained luminosities of $L_{\ast}^{\mathrm{VERA}} = 4950\pm170 L_{\odot}$ and $L_{\ast}^{\mathrm{Gaia}} = 870\pm110 L_{\odot}$. Deduced luminosities also support a validity of the parallax that we determined with VERA. Evaluating the two parallaxes, we concluded that the parallax of 1.73$\pm$0.03 mas from the VERA observations is correct for BX~Cam. We obtained a systemic motion of BX~Cam as (${\mu_{\alpha}}\cos{\delta}^{\mathrm{sys}}$, ${\mu}_{\delta}^{\mathrm{sys}}$) $=$ (13.48$\pm$0.14, $-$34.30$\pm$0.18) mas\,yr$^{-1}$. A total of 73 H$_2$O maser spots detected from our VLBI observations show a spatial distribution of 30 au $\times$ 80 au with a strong elongation along north-south direction. They show outflows with a three-dimensional velocity of 14.79$\pm$1.40 km\,s$^{-1}$. From a comparison between time variations of $V$-band magnitudes and H$_2$O maser, we found that variation of the H$_2$O maser is relevant to that seen in $V$-band even though the H$_2$O maser does not recover its maximum flux in each cycle.
Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations

Variational inference techniques based on inducing variables provide an elegant framework for scalable posterior estimation in Gaussian process (GP) models. Most previous works treat the locations of the inducing variables, i.e. the inducing inputs, as variational hyperparameters, and these are then optimized together with GP covariance hyper-parameters. While some approaches point to the benefits of a Bayesian treatment of GP hyper-parameters, this has been largely overlooked for the inducing inputs. In this work, we show that treating both inducing locations and GP hyper-parameters in a Bayesian way, by inferring their full posterior, further significantly improves performance. Based on stochastic gradient Hamiltonian Monte Carlo, we develop a fully Bayesian approach to scalable GP and deep GP models, and demonstrate its competitive performance through an extensive experimental campaign across several regression and classification problems.
Unveiling the distinct formation pathways of the inner and outer discs of the Milky Way with Bayesian Machine Learning

We develop a Bayesian Machine Learning framework called BINGO (Bayesian INference for Galactic archaeOlogy) centred around a Bayesian neural network. After being trained on the Apache Point Observatory Galactic Evolution Experiment (APOGEE) and Kepler asteroseismic age data, BINGO is used to obtain precise relative stellar age estimates with uncertainties for the APOGEE stars. We carefully construct a training set to minimize bias and apply BINGO to a stellar population that is similar to our training set. We then select the 17 305 stars with ages from BINGO and reliable kinematic properties obtained from Gaia DR2. By combining the age and chemo-kinematical information, we dissect the Galactic disc stars into three components, namely the thick disc (old, high-[α/Fe], [α/Fe] ≳ 0.12), the thin disc (young, low-[α/Fe]), and the Bridge, which is a region between the thick and thin discs. Our results indicate that the thick disc formed at an early epoch only in the inner region, and the inner disc smoothly transforms to the thin disc. We found that the outer disc follows a different chemical evolution pathway from the inner disc. The outer metal-poor stars only start forming after the compact thick disc phase has completed and the star-forming gas disc extended outwardly with metal-poor gas accretion. We found that in the Bridge region the range of [Fe/H] becomes wider with decreasing age, which suggests that the Bridge region corresponds to the transition phase from the smaller chemically well-mixed thick to a larger thin disc with a metallicity gradient.
Framed instanton homology of surgeries on L-space knots

An important class of three-manifolds are L-spaces, which are rational homology spheres with the smallest possible Floer homology. For knots with an instanton L-space surgery, we compute the framed instanton Floer homology of all integral surgeries. As a consequence, if a knot has a Heegaard Floer and instanton Floer L-space surgery, then the theories agree for all integral surgeries. In order to prove the main result, we prove that the Baldwin-Sivek contact invariant in framed instanton Floer homology is homogeneous with respect to the absolute $\mathbb{Z}/2$-grading, but not the $\mathbb{Z}/4$-grading.
Generating Embroidery Patterns Using Image-to-Image Translation

In many scenarios in computer vision, machine learning, and computer graphics, there is a requirement to learn the mapping from an image of one domain to an image of another domain, called Image-to-image translation. For example, style transfer, object transfiguration, visually altering the appearance of weather conditions in an image, changing the appearance of a day image into a night image or vice versa, photo enhancement, to name a few. In this paper, we propose two machine learning techniques to solve the embroidery image-to-image translation. Our goal is to generate a preview image which looks similar to an embroidered image, from a user-uploaded image. Our techniques are modifications of two existing techniques, neural style transfer, and cycle-consistent generative-adversarial network. Neural style transfer renders the semantic content of an image from one domain in the style of a different image in another domain, whereas a cycle-consistent generative adversarial network learns the mapping from an input image to output image without any paired training data, and also learn a loss function to train this mapping. Furthermore, the techniques we propose are independent of any embroidery attributes, such as elevation of the image, light-source, start, and endpoints of a stitch, type of stitch used, fabric type, etc. Given the user image, our techniques can generate a preview image which looks similar to an embroidered image. We train and test our propose techniques on an embroidery dataset which consist of simple 2D images. To do so, we prepare an unpaired embroidery dataset with more than 8000 user-uploaded images along with embroidered images. Empirical results show that these techniques successfully generate an approximate preview of an embroidered version of a user image, which can help users in decision making.
Biomarkers for Risk of Breast Cancer During Pregnancy: A Nested Case Control Study

Background: Several laboratory and epidemiological studies have inversely linked endogenous vitamin D and the risk of breast cancer. The acquisition of vitamin D over time on the relative risk (RR) of the disease development is not known. In a longitudinal study, we evaluated the association between vitamin D levels at pregnancy over time with the risk of breast cancer, and pregnancy-associated breast cancer. Method: The risk for subsequent development of breast cancer associated with serum 25hydroxyvitamin (25-OHD) levels was assessed for consecutive (1st and 2nd pregnancy) samples of 100 cases, with mean lag times (lt) of 7.4 and 4.6 years between sampling and the diagnosis, and matched (parity, age, year, season) controls. Pregnancy-associated breast cancer (PABC, 111 case–control pairs, lt = 1year) risk was also studied. Odds ratios (ORs) with 95% confidence intervals (CI) were calculated using the lowest quintile as the reference. Results: Serum 25-OHD level was not associated with an increased risk neither at the 1st nor at the 2nd pregnancy samples (OR = 1.4, 95%CI 0.6–3.4; OR 1.4, 95%CI 0.7–2.8, respectively), but was associated with an increased risk of PABC (OR = 2.7, 95%CI 1.04–6.7). Conclusion: Generally, vitamin D may not be related to breast cancer risk but the increased PABC risk fits the association of vitamin D with the most aggressive cancers, and warrants caution with vitamin D supplementation during pregnancy. 2009 Elsevier Ltd. All rights reserved.