Miracle-dz/UniFAR-FTUs huggingface dataset:
Interoceptive Accuracy Did Not Affect Moral Decision-Making, but Affect Regret Rating for One’s Moral Choices

Previous studies have revealed the effect of interoceptive accuracy (IAcc), a behavioral measure of the ability to feel physiological states and regulation for that, which origin emotion on decision-making such as gambling. Given that decision-making in moral dilemma situations is affected by emotion, it seems that IAcc also affects moral decision-making. The present study preliminarily investigates whether IAcc affects decision-making and emotional ratings such as regret for one’s own choices in moral dilemma situations. IAcc did not affect moral choice (deontological or utilitarian option), but affected regret ratings for one’s moral choice in portions of dilemma scenarios. Moreover, people with higher IAcc make deontological choices more rapidly than those with lower IAcc in self-related dilemma scenarios. These results suggest that people with higher IAcc feel stronger emotional conflicts about utilitarian choices but weaker conflicts about deontological choices than people with lower IAcc depending on the moral dilemma scenario.
Analysis of the Impact of Livestock Structure on Carbon Emissions of Animal Husbandry: A Sustainable Way to Improving Public Health and Green Environment

Carbon emissions of animal husbandry have been gaining increasing attention due to their high share in global carbon emissions. In this regard, it is essential to assess the regional differences, dynamic evolution patterns, convergence characteristics, and the impact of livestock structure on carbon emissions of animal husbandry. Using data from 30 provincial administrative regions from 2000 to 2018 in China, this study employs the Thiel index method, kernel density analysis, and convergence analysis to quantify the impact of livestock structure on carbon emissions of animal husbandry. The statistical results reveal that carbon emissions of animal husbandry exhibit a rising and declining trend. Specifically, the carbon emissions of animal husbandry are highest in agricultural areas (with a declining trend), followed by agro-pastoral areas (with a declining trend), and the pastoral areas (with a rising trend). It is further revealed that there are no δ convergence and β convergence of carbon emissions of animal husbandry. Finally, essential and useful policy recommendations are put forward to inhibit carbon emissions of animal husbandry.
Team Interdependence as a Substitute for Empowering Leadership Contribution to Team Meaningfulness and Performance

This study uses a relational work design perspective to explore substitutes for leadership behaviors that promote team meaningfulness and performance. We propose that team task interdependence, a structural feature facilitating interaction among team members, can be a substitute for the contributions of empowering leadership. Data were collected from 47 R&D and technology implementation teams across three organizations in a cross-sectional field study. The results revealed that high task interdependence attenuated the contributions of empowering leadership concerning team meaningfulness and, indirectly, to team performance. These findings highlight that the importance of leaders as generators of team meaningfulness is contingent on team relational work design.
RAAS Inhibitor Prescription and Hyperkalemia Event in Patients With Chronic Kidney Disease: A Single-Center Retrospective Study

Hyperkalemia is common in patients treated with renin–angiotensin–aldosterone system inhibitors (RAASis), and it represents the main cause of the large gap reported between guideline recommendations and real-world practice in chronic kidney disease (CKD). We conducted a CKD-population-based restrospective study to determine the prevalence of patients with CKD treated with RAASis, incidence of hyperkalemia in patients with CKD treated with RAASis, and proportion of patients with RAASi medication change after experiencing incident hyperkalemia. Among 809 patients with CKD analyzed, 556 (68.7%) were treated with RAASis, and RAASi prescription was greater in stages 2–4 of CKD. Hyperkalemia occurred in 9.2% of RAASi-treated patients, and the adjusted rate of hyperkalemia among patients with stage 4–5 CKD was 3-fold higher compared with patients with eGFR > 60 ml/min/1.73 m2. RAASi treatment was discontinued in 55.3% of the patients after hyperkalemia event (74.2% discontinued therapy, 3.2% received a reduced dose, and 22.6% reduced the number of RAASi drugs). This study shows that the incidence of hyperkalemia is frequently observed in patients with CKD patients with RAASis, and that rates increase with deteriorating levels of kidney function from stages 1 to 3. RAASi medication change following an episode of hyperkalemia occurred in almost half of the patients after experiencing hyperkalemia.
Association of Headache Disorders and the Risk of Dementia: Meta-Analysis of Cohort Studies

Objectives The purpose of this meta-analysis is to assess whether there is an association between headache disorders and all-cause dementia, Alzheimer's disease (AD), and vascular dementia (VaD). Methods PubMed, Cochrane Library, Embase, and Web of Science were searched for cohort studies published from database inception to October 8, 2021, using medical subject headings (MeSH) and keywords. All statistical analyses were performed using Stata statistical software version 14.0. If P > 0.1 and I2 ≤ 50%, a fixed-effects model was adopted. If I2 > 50% (which indicated great heterogeneity), a random-effects model was adopted. The funnel plot and Egger's test were used to evaluate publication bias. Results This meta-analysis included 12 cohort studies covering 465,358 individuals, which were published between 2001 and 2020. The pooling analysis shows that a history of any headache disorder is associated with an increased risk of all-cause dementia (OR = 1.35; 95% CI: 1.21–1.50; I2 = 81.6%, P < 0.001). The history of any headache was associated with an increased risk of AD (OR = 1.49; 95% CI: 1.08–2.05; I2 = 70.0%, P = 0.003) and VaD (OR = 1.72; 95% CI: 1.32–2.25; I2 = 0%, P < 0.001). In the subgroup analysis, females with a history of headache have a slightly higher risk of dementia than males (OR = 1.32; 95% CI: 1.16–1.51; I2 = 88.3%, P < 0.001) and the risk of dementia in the retrospective cohort was slightly higher than in the prospective cohort (OR = 1.38; 95% CI: 1.22–1.56; I2 = 83.4%, P < 0.001). Conclusions Our meta-analysis shows that any headache disorder increases the risk of all-cause dementia, AD, or VaD. These findings provide evidence that headache should be recognized as an independent risk factor for dementia, AD, or VaD.
The Gender Gap in Life Expectancy in Urban and Rural China, 2013–2018

Background Gender differences in mortality are embedded within mortality transitions. Rural residents generally lag behind their urban counterparts in the transitions. The study objective is to identify major causes of death that drive gender differences in mortality in urban and rural China. Methods We use age-, gender-, urban-rural- and cause-specific mortality data (2013–2018) derived from the national mortality surveillance system that covered about 24% of the Chinese population. We apply Arriaga's method to decompose age- and cause-specific contributions to the gender gap in life expectancy at birth. Analyses are stratified by urban-rural residence. Results Women had a higher life expectancy at birth than men in both urban and rural areas. Cancers, cardiovascular disease, external causes, and respiratory disease accounted for more than 90% of the gender gap in both areas during 2013–2018. In urban areas, the gender gap decreased from 5.17 years in 2013–2015 to 4.98 years in 2016–2018. In rural areas, the gender gap stayed rather constant (2013–2015: 5.68 years; 2016–2018: 5.65 years). Traffic accidents, among external causes, contributed the most to decreasing the gender gap (urban: −0.07 years; rural: −0.10 years), especially in the 0–44 age group. However, the decrease in the gender gap was counteracted by an increase in the gender gap attributable to ischemic heart disease (urban: +0.05 years; rural: +0.08 years) and lung cancer (urban: +0.02 years; rural: +0.05 years) in older age groups. The gender gap attributable either to cerebrovascular disease or to chronic lower respiratory disease decreased in urban areas but increased in rural areas. Conclusions The urban-rural variations in the cause-specific contributions to the gender gap in China suggest the necessity of implementing urban-rural-specific interventions to improve population health and health equity.
Axial Momentum and Quantization of the Majorana Field

New approach to quantization of the relativistic Majorana field is presented. It is based on expansion of the field into eigenfunctions of the axial momentum – a novel observable introduced recently. Relativistic invariance is used as the main guiding principle instead of canonical formalism. Hidden structure of the quantized Majorana field in the form of real Clifford algebra of Hermitian fermionic operators is unveiled. All generators of the Poincaré transformations are found as solutions of certain operator equations, without invoking the principle of correspondence with classical conserved quantities. Also operators of parity P̂ and time reversal T̂ are constructed. *henryk.arodz@uj.edu. pl
Backpropagation Clipping for Deep Learning with Differential Privacy

We present backpropagation clipping, a novel variant of differentially private stochastic gradient descent (DP-SGD) for privacy-preserving deep learning. Our approach clips each trainable layer's inputs (during the forward pass) and its upstream gradients (during the backward pass) to ensure bounded global sensitivity for the layer's gradient; this combination replaces the gradient clipping step in existing DP-SGD variants. Our approach is simple to implement in existing deep learning frameworks. The results of our empirical evaluation demonstrate that backpropagation clipping provides higher accuracy at lower values for the privacy parameter $\epsilon$ compared to previous work. We achieve 98.7% accuracy for MNIST with $\epsilon = 0.07$ and 74% accuracy for CIFAR-10 with $\epsilon = 3.64$.
Moving kinks and their wave packets

Recently a linearized perturbation theory has been formulated for soliton sectors of quantum field theories. While it is more economical than alternative formalisms, such as collective coordinates, it is currently limited to solitons which stay close to a base point, about which the theory is linearized. As a result, so far this formalism has only been applied to stationary solitons. In spite of this limitation, we construct kink states with fixed nonzero momenta and also moving, normalizable kink wave packets. The former are nonnormalizable, coherent superpositions of kinks at all spatial positions and are simultaneous eigenstates of the Hamiltonian and the momentum operator. The latter are localized about a single, moving classical solution. To understand the wave packets, we calculate several simple matrix elements.
Exact solutions of a deep linear network

This work finds the analytical expression for the global minima of a deep linear network with weight decay and stochastic neurons, a fundamental model for understanding the landscape of neural networks. Our result implies that the origin is a special point in the deep neural network loss landscape where highly nonlinear phenomenon emerge. We show that weight decay strongly interacts with the model architecture and can create bad minima at zero in a network with more than one hidden layer, qualitatively different from a network with only one hidden layer. Practically, our result implies that common deep learning initialization methods are generally insufficient to ease the optimization of neural networks.
Differentiable Phonon Simulations To Optimize Thermal Transport in Nanostructures

We introduce a methodology for large-scale optimization of non-Fourier thermal transport in nanostructures, based upon the forward and adjoint phonon Boltzmann transport equation (BTE) and density-based topology optimization. To this end, we also develop the transmission interpolation model (TIM), an interface-based method that allows for smooth interpolation between void and solid regions. We first use our approach to tailor the effective thermal conductivity tensor of a periodic nanomaterial; then, we maximize classical phonon size effects under constrained diffusive transport, obtaining more than a four-fold degradation in the thermal conductivity with respect to commonly-employed configurations. Our method enables systematic optimization of materials for heat management and conversion, and, more broadly, the design of devices where diffusive transport is not valid.
Large-
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 limit of Dicke superradiance

We investigate the thermodynamic limit of Dicke superradiance. We find an expression for the system's density matrix that we can prove is exact in the limit of large atom numbers N. This is in contrast to previously known solutions whose accuracy has only been established numerically and that are valid only for a range of times. We also introduce an asymptotically exact solution when the system is subject to additional incoherent decay of excitations as this is a common occurrence in experiment.
Symmetries of systems of first order ODEs: Symbolic symmetry computations, mechanistic model construction and applications in biology

We discuss the role and merits of symmetry methods for the analysis of biological systems. In particular, we consider systems of first order ordinary differential equations and provide a comprehensive review of the geometrical foundations pertinent to symmetries of such systems. Subsequently, we present an algorithm for finding infinitesimal generators of symmetries for systems with rational reaction terms, and an open-source implementation of the algorithm using symbolic computations. We discuss two complementary perspectives on symmetries in mechanistic modelling; as tools for the analysis of a given model or as a geometrical principle for incorporating biological properties in the construction of new models. Through numerous examples of relevance to modelling in biology we demonstrate the different uses of symmetry methods, and also discuss how to infer symmetries from experimental data. †Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, United Kingdom ‡ Linacre College, University of Oxford, United Kingdom §Department of Mathematics and Mathematical Statistics, Ume̊a University, Sweden ar X iv :2 20 2. 04 93 5v 1 [ qbi o. Q M ] 1 0 Fe b 20 22
A Trust Region Method for Pareto Front Approximation

In this paper, we consider black-box multiobjective optimization problems in which all objective functions are not given analytically. In multiobjective optimization, it is important to produce a set of uniformly distributed discrete solutions over the Pareto front to build a good approximation. In blackbox biobjective optimization, one can evaluate distances between solutions with the ordering property of the Pareto front. These distances allow to be able to evaluate distribution of all solution points, so it is not difficult to maintain uniformity of solutions distribution. However, problems with more than two objectives do not have ordering property, so it is noted that these problems require other techniques to measure the coverage and maintain uniformity of solutions distribution. In this paper, we propose an algorithm based on a trust region method for the Pareto front approximation in black-box multiobjective optimization. In the algorithm, we select a reference point in the set of non-dominated points by employing the density function and explore area around this point. This ensures uniformity of solutions distribution even for problems with more than two objective functions. We also prove that the iteration points of the algorithm converge to Pareto critical points. Finally, we present numerical results suggesting that the algorithm generates the set of well-distributed solutions approximating the Pareto front, even in the case for the problems with three objective functions.
The Triangulum Extended (TREX) Survey: The Stellar Disk Dynamics of M33 as a Function of Stellar Age

Triangulum (M33) is a low-mass, relatively undisturbed spiral galaxy that offers a new regime in which to test models of dynamical heating. In spite of its proximity, M33's dynamical heating history has not yet been well-constrained. In this work, we present the TREX Survey, the largest stellar spectroscopic survey across the disk of M33. We present the stellar disk kinematics as a function of age to study the past and ongoing dynamical heating of M33. We measure line-of-sight velocities for ∼4500 disk stars. Using a subset, we divide the stars into broad age bins using Hubble Space Telescope and Canada–France–Hawaii Telescope photometric catalogs: massive main-sequence stars and helium-burning stars (∼80 Myr), intermediate-mass asymptotic branch stars (∼1 Gyr), and low-mass red giant branch stars (∼4 Gyr). We compare the stellar disk dynamics to that of the gas using existing H i, CO, and Hα kinematics. We find that the disk of M33 has relatively low-velocity dispersion (∼16 km s−1), and unlike in the Milky Way and Andromeda galaxies, there is no strong trend in velocity dispersion as a function of stellar age. The youngest disk stars are as dynamically hot as the oldest disk stars and are dynamically hotter than predicted by most M33-like low-mass simulated analogs in Illustris. The velocity dispersion of the young stars is highly structured, with the large velocity dispersion fairly localized. The cause of this high-velocity dispersion is not evident from the observations and simulated analogs presented here.
DeepCENT: Prediction of Censored Event Time via Deep Learning

With the rapid advances of deep learning, many computational methods have been developed to analyze nonlinear and complex right censored data via deep learning approaches. However, the majority of the methods focus on predicting survival function or hazard function rather than predicting a single valued time to an event. In this paper, we propose a novel method, DeepCENT, to directly predict the individual time to an event. It utilizes the deep learning framework with an innovative loss function that combines the mean square error and the concordance index. Most importantly, DeepCENT can handle competing risks, where one type of event precludes the other types of events from being observed. The validity and advantage of DeepCENT were evaluated using simulation studies and illustrated with three publicly available cancer data sets.
Creating an institutional ecosystem for cash transfer programming: Lessons from post-disaster governance in Indonesia (preprint)

Humanitarian and disaster management actors have increasingly adopted cash transfer to reduce the sufferings and vulnerability of the survivors. Case transfers have also been used as a critical instrument in the current COVID-19 pandemic. Unfortunately, academic work on humanitarian and disaster-cash transfer related issues remains limited. This article explores how NGOs and governments implement humanitarian cash transfer in a post-disaster setting using an exploratory research strategy. It asks What are institutional constraints and opportunities faced by humanitarian emergency responders in ensuring an effective humanitarian cash transfer and how humanitarian actors address such institutional conditions. We introduced a new conceptual framework, namely humanitarian and disaster management ecosystem for cash transfer. This framework allows non-governmental actors to restore complex relations between the state, disaster survivors or citizen, local market economy and civil society. Mixed methods and multistage research strategy were used to collect and analyze primary and secondary data. The findings suggest that implementing cash transfers in the context of post tsunamigenic earthquakes and liquefaction hazards, NGOs must co-create an ecosystem of response that not only aimed at restoring peoples access to cash and basic needs but first they must restore relations between the states and their citizen while linking the at-risk communities with the private sectors to jump-starting local livelihoods and market economy.
Semi-Supervised Convolutive NMF for Automatic Music Transcription

Automatic Music Transcription, which consists in transforming an audio recording of a musical performance into symbolic format, remains a difﬁcult Music Information Retrieval task. In this work, we propose a semi-supervised approach using low-rank matrix factorization techniques, in particular Convolutive Nonnegative Matrix Factorization. In the semi-supervised setting, only a single recording of each individual notes is required. We show on the MAPS dataset that the proposed semi-supervised CNMF method performs better than state-of-the-art low-rank factorization techniques and a little worse than supervised deep learning state-of-the-art methods, while however suffering from generalization issues.
A Coupled CP Decomposition for Principal Components Analysis of Symmetric Networks

—In a number of application domains, one observes a sequence of network data; for example, repeated measurements between users interactions in social media platforms, ﬁnancial correlation networks over time, or across subjects, as in multi-subject studies of brain connectivity. One way to analyze such data is by stacking networks into a third-order array or tensor. We propose a principal components analysis (PCA) framework for sequence network data, based on a novel decomposition for semi-symmetric tensors. We derive efﬁcient algorithms for computing our proposed “Coupled CP” decomposition and establish estimation consistency of our approach under an analogue of the spiked covariance model with rates the same as the matrix case up to a logarithmic term. Our framework inherits many of the strengths of classical PCA and is suitable for a wide range of unsupervised learning tasks, including identifying principal networks, isolating meaningful changepoints or outliers across observations, and for characterizing the “variability network” of the most varying edges. Finally, we demonstrate the effectiveness of our proposal on simulated data and on examples from political science and ﬁnancial economics. The proof techniques used to establish our main consistency results are surprisingly straight-forward and may ﬁnd use in a variety of other matrix and tensor decomposition problems.
Visual Servoing for Pose Control of Soft Continuum Arm in a Structured Environment

For soft continuum arms, visual servoing is a popular control strategy that relies on visual feedback to close the control loop. However, robust visual servoing is challenging as it requires reliable feature extraction from the image, accurate control models and sensors to perceive the shape of the arm, both of which can be hard to implement in a soft robot. This letter circumvents these challenges by presenting a deep neural network-based method to perform smooth and robust 3D positioning tasks on a soft arm by visual servoing using a camera mounted at the distal end of the arm. A convolutional neural network is trained to predict the actuations required to achieve the desired pose in a structured environment. Integrated and modular approaches for estimating the actuations from the image are proposed and are experimentally compared. A proportional control law is implemented to reduce the error between the desired and current image as seen by the camera. The model together with the proportional feedback control makes the described approach robust to several variations such as new targets, lighting, loads, and diminution of the soft arm. Furthermore, the model lends itself to be transferred to a new environment with minimal effort.