Miracle-dz/UniFAR-FTUs huggingface dataset:
A cell‐centered finite volume formulation of geometrically exact Simo–Reissner beams with arbitrary initial curvatures

This article presents a novel total Lagrangian cell‐centered finite volume formulation of geometrically exact beams with arbitrary initial curvatures undergoing large displacements and finite rotations. The choice of rotation parameterization, the mathematical formulation of the beam kinematics, conjugate strain measures, and the linearization of the strong form of governing equations are described. The finite volume based discretization of the computational domain and the governing equations for each computational volume are presented. The discretized integral form of the equilibrium equations is solved using a block‐coupled Newton–Raphson solution procedure. The efficacy of the proposed methodology is presented by comparing the simulated numerical results with classic benchmark test cases available in the literature. The objectivity of strain measures for the current formulation and mesh convergence studies for both initially straight and curved beam configurations are also discussed.
Unsupervised Conversation Disentanglement through Co-Training

Conversation disentanglement aims to separate intermingled messages into detached sessions, which is a fundamental task in understanding multi-party conversations. Existing work on conversation disentanglement relies heavily upon human-annotated datasets, which is expensive to obtain in practice. In this work, we explore training a conversation disentanglement model without referencing any human annotations. Our method is built upon the deep co-training algorithm, which consists of two neural networks: a message-pair classifier and a session classifier. The former is responsible of retrieving local relations between two messages while the latter categorizes a message to a session by capturing context-aware information. Both the two networks are initialized respectively with pseudo data built from the unannotated corpus. During the deep co-training process, we use the session classifier as a reinforcement learning component to learn a session assigning policy by maximizing the local rewards given by the message-pair classifier. For the message-pair classifier, we enrich its training data by retrieving message pairs with high confidence from the disentangled sessions predicted by the session classifier. Experimental results on the large Movie Dialogue Dataset demonstrate that our proposed approach achieves competitive performance compared to previous supervised methods. Further experiments show that the predicted disentangled conversations can promote the performance on the downstream task of multi-party response selection.
Information Payoffs: An Interim Perspective

We study the payoffs that can arise under some information structure from an interim perspective. There is a set of types distributed according to some prior distribution and a payoff function that assigns a value to each pair of a type and a belief over the types. Any information structure induces an interim payoff proﬁle which describes, for each type, the expected payoff under the information structure conditional on the type. We characterize the set of all interim payoff proﬁles consistent with some information structure. We illustrate our results through applications.
Ignorable and non-ignorable missing data in hidden Markov models

We consider missing data in the context of hidden Markov models with a focus on situations where data is missing not at random (MNAR) and missingness depends on the identity of the hidden states. In simulations, we show that including a submodel for state-dependent missingness reduces bias when data is MNAR and state-dependent, whilst not reducing accuracy when data is missing at random (MAR). When missingness depends on time but not the hidden states, a model which only allows for state-dependent missingness is biased, whilst a model that allows for both stateand time-dependent missingness is not. Overall, these results show that modelling missingness as state-dependent, and including other relevant covariates, is a useful strategy in applications of hidden Markov models to time-series with missing data. We conclude with an application of the stateand time-dependent MNAR hidden Markov model to a real dataset, involving severity of schizophrenic symptoms in a clinical trial.
High-fidelity magic-state preparation with a biased-noise architecture

Magic state distillation is a resource intensive subroutine that consumes noisy input states to produce high-ﬁdelity resource states that are used to perform logical operations in practical quantum-computing architectures. The resource cost of magic state distillation can be reduced by improving the ﬁdelity of the raw input states. To this end, we propose an initialization protocol that offers a quadratic improvement in the error rate of the input magic states in architectures with biased noise. This is achieved by preparing an error-detecting code which detects the dominant errors that occur during state preparation. We obtain this advantage by exploiting the native gate operations of an underlying qubit architecture that experiences biases in its noise proﬁle. We perform simulations to analyze the performance of our protocol with the XZZX surface code. Even at modest physical parameters with a two-qubit gate error rate of 0 . 7% and total probability of dominant errors in the gate O (10 3 ) larger compared to that of nondominant errors, we ﬁnd that our preparation scheme delivers magic states with logical error rate O (10 − 8 ) after a single round of the standard 15-to-1 distillation protocol, two orders of magnitude lower than using conventional state preparation. Our approach therefore promises considerable savings in overheads with near-term technology.
KMS states for generalized gauge actions on C*-algebras associated with self-similar sets

Given a self-similar K set defined from an iterated function system Γ = (γ1, . . . , γd) and a set of function H = {hi : K → R}i=1 satisfying suitable conditions, we define a generalized gauge action on Kawjiwara-Watatani algebras OΓ and their Toeplitz extensions TΓ. We then characterize the KMS states for this action. For each β ∈ (0,∞), there is a Ruelle operator LH,β and the existence of KMS states at inverse temperature β is related to this operator. The critical inverse temperature βc is such that LH,βc has spectral radius 1. If β < βc, there are no KMS states on OΓ and TΓ; if β = βc, there is a unique KMS state on OΓ and TΓ which is given by the eigenmeasure of LH,βc ; and if β > βc, including β = ∞, the extreme points of the set of KMS states on TΓ are parametrized by the elements of K and on OΓ by the set of branched points.
Theoretical effects of decreasing saturated fat and cholesterol intake on total serum and LDL Cholesterol Levels

The National Cholesterol Education Program (NCEP) recommends a low density lipoprotein (LDL) cholesterol goal above which dietary or drug therapy should be initiated. The NCEP recommends that individuals exceeding their LDL cholesterol goal consume a “Step I” diet with a maximum intake of 10% of calories as saturated fat and 300 mg of cholesterol. The NCEP also recommends a “Step II” diet, with a maximum of 7% of calories as saturated fat and 200 mg of cholesterol, to those individuals unable to meet their LDL cholesterol goal by the consumption of the Step I diet. In order to evaluate the effectiveness of the NCEP Step diets, data from the first phase of the National Health and Nutrition Examination Survey (NHANES) III (conducted between 1988 and 1991) were analyzed. Using the 1993 Hegsted equation for the prediction of changes in LDL cholesterol levels resulting from changes in saturated fat and cholesterol, it was estimated that most individuals would be unable to meet their LDL cholesterol goal by changes to the Step diets. Further, using the 1993 Hegsted equation for the prediction of changes in total serum cholesterol resulting from changes in saturated fat and dietary cholesterol, it was estimated that most individuals would be unable to lower their total serum cholesterol to less than 180 mg/dl if they consumed the NCEP Step diets. A level of more than 180 mg/dl of total cholesterol has been shown in the M ultiple Risk Factor Intervention Trial to be associated with an increased risk of death from CHD. Therefore, most individuals need some additional dietary therapy other than the NCEP Step II diet to lower their total serum cholesterol to less than 180 mg/dl. Using data from the Nationwide Food Consumption Survey (NFCS) 1987-88, it was found that the consumption of a low percentage of energy as saturated fat is associated with low percent intakes of the RDAs for zinc, calcium, magnesium and riboflavin. Therefore, individuals wishing to lower their saturated fat intake must make special efforts must be made to ensure an adequate intake of these micronutrients.
Social Psychology of Education The Role of Passion for Studies on Academic Procrastination and Mental Health During the COVID-19 Pandemic

Procrastination is a maladaptive behaviour that students often experience in academic activities and can result in negative consequences to mental health. The challenges imposed by the COVID-19 pandemic can contribute to increase procrastination behaviors in academic activities that the student does not like and in those he/she is passionate. The main objective of this research was to test an integrative model of passion, procrastination, satisfaction with life and psychological distress in students during pandemic. The sample was comprised of 416 university students aged between 18 and 57 years (Mage = 24.81 ± 7.02, 78.1% women). Structural Equation Modeling results revealed that academic procrastination negatively linked to harmonious passion, and positively linked to obsessive passion. Academic procrastination in turn negatively linked to satisfaction with life and positively linked to psychological distress. Harmonious passion also was directly positively associated to satisfaction with life and negatively associated to psychological distress. These results suggest that students’ harmonious passion for their studies plays a protective role against academic procrastination and mental health indicators, while obsessive passion represents a risk factor.
Capturing Bias in the Liberal and Conservative News Sources Anonymous ACL submission

Many people try to become aware of the important events happening in the world through reading news articles from well-known news outlets. Many of the big events and decisions, directly or indirectly affects our lives, so it is important to know about them and make reactions if needed. Unfortunately most of the news sources are politically biased and they convey the news in a way to give the reader an opinion close to themselves. The inherited bias in NLP models such as word embedding is a critical problem affecting all social applications of them. However, due to vague notion of the bias
Exploring Functional Differences between the Right and Left Ventricles to Better Understand Right Ventricular Dysfunction

The right and left ventricles have traditionally been studied as individual entities. Furthermore, modifications found in diseased left ventricles are assumed to influence on right ventricle alterations, but the connection is poorly understood. In this review, we describe the differences between ventricles under physiological and pathological conditions. Understanding the mechanisms that differentiate both ventricles would facilitate a more effective use of therapeutics and broaden our knowledge of right ventricle (RV) dysfunction. RV failure is the strongest predictor of mortality in pulmonary arterial hypertension, but at present, there are no definitive therapies directly targeting RV failure. We further explore the current state of drugs and molecules that improve RV failure in experimental therapeutics and clinical trials to treat pulmonary arterial hypertension and provide evidence of their potential benefits in heart failure.
Understanding the Social Determinants of Mental Health of Undergraduate Students in Bangladesh: Interview Study

Background The undergraduate student population has been actively studied in digital mental health research. However, the existing literature primarily focuses on students from high-income nations, and undergraduates from limited-income nations remain understudied. Objective This study aims to identify the broader social determinants of mental health among undergraduate students in Bangladesh, a limited-income nation in South Asia; study the manifestation of these determinants in their day-to-day lives; and explore the feasibility of self-monitoring tools in helping them identify the specific factors or relationships that affect their mental health. Methods We conducted a 21-day study with 38 undergraduate students from 7 universities in Bangladesh. We conducted 2 semistructured interviews: one prestudy and one poststudy. During the 21-day study, participants used an Android app to self-report and self-monitor their mood after each phone conversation. The app prompted participants to report their mood after each phone conversation and provided graphs and charts so that the participants could independently review their mood and conversation patterns. Results Our results show that academics, family, job and economic condition, romantic relationship, and religion are the major social determinants of mental health among undergraduate students in Bangladesh. Our app helped the participants pinpoint the specific issues related to these factors, as the participants could review the pattern of their moods and emotions from past conversation history. Although our app does not provide any explicit recommendation, the participants took certain steps on their own to improve their mental health (eg, reduced the frequency of communication with certain persons). Conclusions Although some of the factors (eg, academics) were reported in previous studies conducted in the Global North, this paper sheds light on some new issues (eg, extended family problems and religion) that are specific to the context of the Global South. Overall, the findings from this study would provide better insights for researchers to design better solutions to help the younger population from this part of the world.
Configuration spaces over singular spaces -- I. Dirichlet-Form and Metric Measure Geometry

We construct a canonical differential structure on the configuration space $\Upsilon$ over a singular base space $X$ and with a general invariant measure $\mu$ on $\Upsilon$. We present an analytic structure on $\Upsilon$, constructing a strongly local Dirichlet form $\mathcal E$ on $L^2(\Upsilon, \mu)$ for $\mu$ in a large class of probability measures. We then investigate the geometric structure of the extended metric measure space $\Upsilon$ endowed with the $L^2$-transportation extended distance $\mathsf{d}_{\Upsilon}$ and with the measure $\mu$. By establishing Rademacher- and Sobolev-to-Lipschitz-type properties for $\mathcal E$, we finally provide a complete identification of the analytic and the geometric structure -- the canonical differential structure induced on $\Upsilon$ by $X$ and $\mu$ -- showing that $\mathcal E$ coincides with the Cheeger energy of $(\Upsilon,\mathsf{d}_{\Upsilon},\mu)$ and that the intrinsic distance of $\mathcal E$ coincides with $\mathsf{d}_{\Upsilon}$. The class of base spaces to which our results apply includes sub-Riemannian manifolds, RCD spaces, and path/loop spaces over Riemannian manifolds; as for $\mu$ our results include quasi-Gibbs measures, in particular: Poisson measures, canonical Gibbs measures, as well as some determinantal/permanental point processes (sine$_\beta$, Airy$_\beta$, Bessel$_{\alpha,\beta}$, Ginibre). A number of applications to interacting particle systems and infinite-dimensional metric measure geometry are also discussed. In particular, we prove the universality of the $L^2$-transportation distance $\mathsf{d}_{\Upsilon}$ for the Varadhan short-time asymptotics for diffusions on $\Upsilon$, regardless of the choice of $\mu$. Many of our results are new even in the case of configuration spaces over Euclidean spaces.
Prevalence of Antenatal Depression and Associated Factors among Pregnant Women Attending Antenatal Care at Health Institutions of Faafan Zone, Somali Region, Eastern Ethiopia

Background Depression is a common global mental health tragedy which affects more than 30 million people of all ages. Antenatal depression is higher among low-income countries where maternal and psychosocial factors act as determinant factors for its occurrence. Aim This study is aimed at assessing the prevalence of antenatal depression and its associated factors among pregnant women attending health institutions of Faafan zone of Somali regional state, Eastern Ethiopia. Method An institutional-based cross-sectional study design was conducted among randomly selected 403 pregnant women from January to September 2015. EPDS with 13 cutoff points was used to screen antenatal depression. Bivariate and multivariate logistic regressions were used to identify associated factors. Result The study showed that 24.3% of women had antenatal depression. Marital status, educational status, chronic medical illness, previous depression history, and social support were factors associated with antenatal depression. Conclusion The study revealed that the prevalence of antenatal depression was 24.3%. Ethiopia Federal Ministry of Health and Somali Regional Health Bureau should work very hard to create awareness on the importance of pregnancy planning and social support during pregnancy.
Scaling K2. IV. A Uniform Planet Sample for Campaigns 1–8 and 10–18

We provide the first full K2 transiting exoplanet sample, using photometry from Campaigns 1–8 and 10–18, derived through an entirely automated procedure. This homogeneous planet candidate catalog is crucial to perform a robust demographic analysis of transiting exoplanets with K2. We identify 747 unique planet candidates and 57 multiplanet systems. Of these candidates, 366 have not been previously identified, including one resonant multiplanet system and one system with two short-period gas giants. By automating the construction of this list, measurements of sample biases (completeness and reliability) can be quantified. We carried out a light-curve-level injection/recovery test of artificial transit signals and found a maximum completeness of 61%, a consequence of the significant detrending required for K2 data analysis. Through this operation we attained measurements of the detection efficiency as a function of signal strength, enabling future population analysis using this sample. We assessed the reliability of our planet sample by testing our vetting software EDI-Vetter against inverted transit-free light curves. We estimate that 91% of our planet candidates are real astrophysical signals, increasing up to 94% when limited to the FGKM dwarf stellar population. We also constrain the contamination rate from background eclipsing binaries to less than 5%. The presented catalog, along with the completeness and reliability measurements, enable robust exoplanet demographic studies to be carried out across the fields observed by the K2 mission for the first time.
Safe-Critical Modular Deep Reinforcement Learning with Temporal Logic through Gaussian Processes and Control Barrier Functions

—Reinforcement learning (RL) is a promising approach and has limited success towards real-world applications, because ensuring safe exploration or facilitating adequate exploitation is a challenges for controlling robotic systems with unknown models and measurement uncertainties. Such a learning problem becomes even more intractable for complex tasks over continuous space (state-space and action-space). In this paper, we propose a learning-based control framework consisting of several aspects: (1) linear temporal logic (LTL) is leveraged to facilitate complex tasks over an inﬁnite horizons which can be translated to a novel automaton structure; (2) we propose an innovative reward scheme for RL-agent with the formal guarantee such that global optimal policies maximize the probability of satisfying the LTL speciﬁcations; (3) based on a reward shaping technique, we develop a modular policy-gradient architecture utilizing the beneﬁts of automaton structures to decompose overall tasks and facilitate the performance of learned controllers; (4) by incorporating Gaussian Processes (GPs) to estimate the uncertain dynamic systems, we synthesize a model-based safeguard using Exponential Control Barrier Functions (ECBFs) to address problems with high-order relative degrees. In addition, we utilize the properties of LTL automatons and ECBFs to construct a guiding process to further improve the efﬁciency of exploration. Finally, we demonstrate the effectiveness of the framework via several robotic environments. And we show such an ECBF-based modular deep RL algorithm achieves near-perfect success rates and guard safety with a high probability conﬁdence during training.
CovarianceNet: Conditional Generative Model for Correct Covariance Prediction in Human Motion Prediction

The correct characterization of uncertainty when predicting human motion is equally important as the accuracy of this prediction. We present a new method to correctly predict the uncertainty associated with the predicted distribution of future trajectories. Our approach, CovariaceNet, is based on a Conditional Generative Model with Gaussian latent variables in order to predict the parameters of a bi-variate Gaussian distribution. The combination of CovarianceNet with a motion prediction model results in a hybrid approach that outputs a uni-modal distribution. We will show how some state of the art methods in motion prediction become overconfident when predicting uncertainty, according to our proposed metric and validated in the ETH data-set [1]. CovarianceNet correctly predicts uncertainty, which makes our method suitable for applications that use predicted distributions, e.g., planning or decision making.
Quasiparticle band structures, spontaneous polarization, and spin-splitting in noncentrosymmetric few-layer and bulk γ-GeSe

Group-IV monochalcogenides have attracted much attention due to their potential of ferroelectric and multiferroic properties. Recently, centrosymmetric γ-phase GeSe in a double-layer honeycomb lattice has been theoretically predicted, but the synthesized γ-phase GeSe showed a noncentrosymmetric atomic structure, leading to the possibility of ferroelectricity and spin-splitting. Here, we study the quasiparticle band structures, spontaneous polarization, and spin-splitting in noncentrosymmetric γ-GeSe using density functional theory and GW calculations. Our results show that noncentrosymmetric few-layer and bulk γ-GeSe have semiconducting band structures with indirect band gaps, which depend almost linearly on the reciprocal of the number of layers. Spontaneous polarization occurs due to a small charge transfer between the layers, which increases with compressive strain, and ferroelectric switching can be achieved by an interlayer translation with a small energy barrier. Spin-splitting is found to be more significant at the highest valence band than at the lowest conduction band. Our results provide insights into the fundamental electronic properties of a layered ferroelectric semiconductor applicable to devices with ferroelectric/nonferroelectric junctions.