Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceptions and acceptance of COVID-19 vaccine among pregnant and lactating women in Singapore: A cross-sectional study

Introduction Vaccination is critical in controlling the coronavirus disease 19 (COVID-19) pandemic. However, vaccine perception and acceptance among pregnant and lactating women is unknown in Singapore. We aimed to determine the acceptance of COVID-19 vaccination among these two groups of women in Singapore, and factors associated with vaccine acceptance. Methods We conducted an anonymous, online survey on the perception and acceptance of the COVID-19 vaccine in pregnant and lactating women at a tertiary hospital in Singapore from 1st March to 31st May 2021. Information on demographics and knowledge were collected, and these factors were assessed for their relationship with vaccine acceptance. Results A total of 201 pregnant and 207 lactating women participated. Vaccine acceptance rates in pregnant and lactating women were 30.3% and 16.9% respectively. Pregnant women who were unsure or unwilling to take the vaccine cited concerns about safety of the vaccine during pregnancy (92.9%), while lactating women were concerned about potential long-term negative effects on the breastfeeding child (75.6%). Other factors significantly associated with vaccine acceptance included a lower monthly household income or education level, appropriate knowledge regarding vaccine mechanism and higher perceived maternal risk of COVID-19. Most pregnant (70.0%) and lactating women (83.7%) were willing to take the vaccine only when more safety data during pregnancy and breastfeeding were available. Conclusions COVID-19 vaccine acceptance was low among pregnant and lactating women in Singapore. Addressing safety concerns when more data is available and education on mechanism of vaccine action will likely improve acceptance among these women.
Can Environmental Policy Encourage Technical Change? Emissions Taxes and R&D Investment in Polluting Firms

Higher country taxes on noxious manufacturing emissions lead to substantial increases in firm R&D spending. The R&D response is driven entirely by the high-pollution firms most affected by emissions taxes. Pollution taxes increase the marginal value of R&D spending in polluting firms, even when this spending does not lead to new innovation. Pollution taxes have the strongest effect on R&D investment in sectors where new invention is harder to appropriate and outside knowledge is easier to acquire, suggesting an important reason dirty firms invest in R&D is to expand their capacity to absorb external knowledge and technical know-how.
Random Utility and Limited Consideration

The random utility model (RUM, McFadden and Richter (1990)) has been the standard tool to describe the behavior of a population of decision makers. RUM assumes that decision makers behave as if they maximize a rational preference over a choice set. This assumption may fail when consideration of all alternatives is costly. We provide a theoretical and statistical framework that unifies well‐known models of random (limited) consideration and generalizes them to allow for preference heterogeneity. We apply this methodology in a novel stochastic choice data set that we collected in a large‐scale online experiment. Our data set is unique since it exhibits both choice set and (attention) frame variation. We run a statistical survival race between competing models of random consideration and RUM. We find that RUM cannot explain the population behavior. In contrast, we cannot reject the hypothesis that decision makers behave according to the logit attention model (Brady and Rehbeck (2016)).
Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE

InfoNCE-based contrastive representation learners, such as SimCLR, have been tremendously successful in recent years. However, these contrastive schemes are notoriously resource demanding, as their effectiveness breaks down with small-batch training (i.e., the log-K curse, whereas K is the batch-size). In this work, we reveal mathematically why contrastive learners fail in the small-batch-size regime, and present a novel simple, non-trivial contrastive objective named FlatNCE, which fixes this issue. Unlike InfoNCE, our FlatNCE no longer explicitly appeals to a discriminative classification goal for contrastive learning. Theoretically, we show FlatNCE is the mathematical dual formulation of InfoNCE, thus bridging the classical literature on energy modeling; and empirically, we demonstrate that, with minimal modification of code, FlatNCE enables immediate performance boost independent of the subject-matter engineering efforts. The significance of this work is furthered by the powerful generalization of contrastive learning techniques, and the introduction of new tools to monitor and diagnose contrastive training. We substantiate our claims with empirical evidence on CIFAR10, ImageNet, and other datasets, where FlatNCE consistently outperforms InfoNCE.
Pose-Based Tactile Servoing: Controlled Soft Touch Using Deep Learning

This article describes a new way of controlling robots using soft tactile sensors: pose-based tactile servo (PBTS) control. The basic idea is to embed a tactile perception model for estimating the sensor pose within a servo control loop that is applied to local object features, such as edges and surfaces. PBTS control is implemented with a soft, curved optical tactile sensor [the Bristol Robotics Laboratory (BRL) TacTip] using a convolutional neural network trained to be insensitive to shear. As a consequence, robust and accurate controlled motion over various complex 3D objects is attained.
SHADOWCAST: Controllable Graph Generation with Explainability.

We introduce the problem of explaining graph generation, formulated as controlling the generative process to produce desired graphs with explainable structures. By directing this generative process, we can explain the observed outcomes. We propose SHADOWCAST, a controllable generative model capable of mimicking networks and directing the generation, as an approach to this novel problem. The proposed model is based on a conditional generative adversarial network for graph data. We design it with the capability to control the conditions using a simple and transparent Markov model. Comprehensive experiments on three real-world network datasets demonstrate our model's competitive performance in the graph generation task. Furthermore, we control SHADOWCAST to generate graphs of different structures to show its effective controllability and explainability. As the first work to pose the problem of explaining generated graphs by controlling the generation, SHADOWCAST paves the way for future research in this exciting area.
Habits and Demand Changes after COVID-19

In this paper, we investigate how the COVID-19 pandemics and more precisely the lockdown of a sector of the economy may have changed our habits and, therefore, altered the demand of some goods even after the re-opening. In a two-sector infinite horizon economy, we show that the demand of the goods produced by the sector closed during the lockdown could shrink or expand with respect to their prepandemic level depending on the length of the lockdown and the relative strength of the satiation effect and the substitutability effect. We also provide conditions under which this sector could remain inactive even after the lockdown as well as an insight on the policy which should be adopted to avoid this outcome.
Two-phase geothermal model with fracture network and multi-branch wells

This paper focuses on the numerical simulation of geothermal systems in complex geological settings. The physical model is based on two-phase Darcy flows coupling the mass conservation of the water component with the energy conservation and the liquid vapor thermodynamical equilibrium. The discretization exploits the flexibility of unstructured meshes to model complex geology including conductive faults as well as complex wells. The polytopal and essentially nodal Vertex Approximate Gradient scheme is used for the approximation of the Darcy and Fourier fluxes combined with a Control Volume approach for the transport of mass and energy. Particular attention is paid to the faults which are modelled as two-dimensional interfaces defined as collection of faces of the mesh and to the flow inside deviated or multi-branch wells defined as collection of edges of the mesh with rooted tree data structure. By using an explicit pressure drop calculation, the well model reduces to a single equation based on complementarity constraints with only one well implicit unknown. The coupled systems are solved fully implicitely at each time step using efficient nonlinear and linear solvers on parallel distributed architectures. The convergence of the discrete model is investigated numerically on a simple test case with a Cartesian geometry and a single vertical producer well. Then, the ability of our approach to deal efficiently with realistic test cases is assessed on a high energy faulted geothermal reservoir operated using a doublet of two deviated wells.
Interplay between singlet and triplet pairings in multiband two-dimensional oxide superconductors

L. Lepori, 2, ∗ D. Giuliano, 2 A. Nava, 2 and C. A. Perroni 4 Dipartimento di Fisica, Universitá della Calabria, Arcavacata di Rende I-87036, Cosenza, Italy. I.N. F.N. , Gruppo collegato di Cosenza, Arcavacata di Rende I-87036, Cosenza, Italy. Dipartmento di Fisica ”Ettore Pancini”, Università degli Studi di Napoli Federico II, Complesso Universitario Monte S. Angelo, Via Cintia, I-80126 Napoli, Italy. CNR-SPIN c/o Università degli Studi di Napoli Federico II, Complesso Universitario Monte S. Angelo, Via Cintia, I-80126 Napoli, Italy.
Quantum-Optimal Object Discrimination in Sub-Diffraction Incoherent Imaging

We consider imaging tasks involving discrimination between known objects and investigate the best possible accuracy with which the correct object can be identified. Using the quantum Chernoff bound, we analytically find the ultimate achievable asymptotic error rate for symmetric hypothesis tests between any two incoherent 2D objects when the imaging system is dominated by optical diffraction. Furthermore, we show that linear-optical demultiplexing of the spatial modes of the collected light exactly saturates this ultimate performance limit, enabling a quadratic improvement over the asymptotic error rate achieved by direct imaging as the objects become more severely diffraction-limited. We extend our results to identify the quantum limit and optimal measurement for discrimination between an arbitrary number of candidate objects. Our work constitutes a complete theoretical treatment of the ultimate quantitative limits on passive, sub-diffraction, incoherent object discrimination and is readily applicable to a multitude of real-world applications.
Backward-Compatible Prediction Updates: A Probabilistic Approach

When machine learning systems meet real world applications, accuracy is only one of several requirements. In this paper, we assay a complementary perspective originating from the increasing availability of pre-trained and regularly improving state-of-the-art models. While new improved models develop at a fast pace, downstream tasks vary more slowly or stay constant. Assume that we have a large unlabelled data set for which we want to maintain accurate predictions. Whenever a new and presumably better ML models becomes available, we encounter two problems: (i) given a limited budget, which data points should be re-evaluated using the new model? ; and (ii) if the new predictions differ from the current ones, should we update? Problem (i) is about compute cost, which matters for very large data sets and models. Problem (ii) is about maintaining consistency of the predictions, which can be highly relevant for downstream applications; our demand is to avoid negative flips, i.e., changing correct to incorrect predictions. In this paper, we formalize the Prediction Update Problem and present an efficient probabilistic approach as answer to the above questions. In extensive experiments on standard classification benchmark data sets, we show that our method outperforms alternative strategies along key metrics for backward-compatible prediction updates.
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning

Inducing causal relationships from observations is a classic problem in machine learning. Most work in causality starts from the premise that the causal variables themselves are observed. However, for AI agents such as robots trying to make sense of their environment, the only observables are low-level variables like pixels in images. To generalize well, an agent must induce high-level variables, particularly those which are causal or are affected by causal variables. A central goal for AI and causality is thus the joint discovery of abstract representations and causal structure. However, we note that existing environments for studying causal induction are poorly suited for this objective because they have complicated task-specific causal graphs which are impossible to manipulate parametrically (e.g., number of nodes, sparsity, causal chain length, etc.). In this work, our goal is to facilitate research in learning representations of high-level variables as well as causal structures among them. In order to systematically probe the ability of methods to identify these variables and structures, we design a suite of benchmarking RL environments. We evaluate various representation learning algorithms from the literature and find that explicitly incorporating structure and modularity in models can help causal induction in model-based reinforcement learning.
The Optimal Size of an Epistemic Congress

We analyze the optimal size of a congress in a representative democracy. We take an epistemic view where voters decide on a binary issue with one ground truth outcome, and each voter votes correctly according to their competence levels in $[0, 1]$. Assuming that we can sample the best experts to form an epistemic congress, we find that the optimal congress size should be linear in the population size. This result is striking because it holds even when allowing the top representatives to be accurate with arbitrarily high probabilities. We then analyze real world data, finding that the actual sizes of congresses are much smaller than the optimal size our theoretical results suggest. We conclude by analyzing under what conditions congresses of sub-optimal sizes would still outperform direct democracy, in which all voters vote.
Nearly constant Q dissipative models and wave equations for general viscoelastic anisotropy

The quality factor (Q) links seismic wave energy dissipation to physical properties of the Earth’s interior, such as temperature, stress and composition. Frequency independence of Q, also called constant Q for brevity, is a common assumption in practice for seismic Q inversions. Although exactly and nearly constant Q dissipative models are proposed in the literature, it is inconvenient to obtain constant Q wave equations in differential form, which explicitly involve a specified Q parameter. In our recent research paper, we proposed a novel weighting function method to build the first- and second-order nearly constant Q dissipative models. Of importance is the fact that the wave equations in differential form for these two models explicitly involve a specified Q parameter. This behaviour is beneficial for time-domain seismic waveform inversion for Q, which requires the first derivative of wavefields with respect to Q parameters. In this paper, we extend the first- and second-order nearly constant Q models to the general viscoelastic anisotropic case. We also present a few formulations of the nearly constant Q viscoelastic anisotropic wave equations in differential form.
EMG-Based Feature Extraction and Classification for Prosthetic Hand Control

In recent years, real-time control of prosthetic hands has gained a great deal of attention. In particular, real-time analysis of Electromyography (EMG) signals has several challenges to achieve an acceptable accuracy and execution delay. In this paper, we address some of these challenges by improving the accuracy in a shorter signal length. We first introduce a set of new feature extraction functions applying on each level of wavelet decomposition. Then, we propose a postprocessing ap- proach to process the neural network outputs. The experimental results illustrate that the proposed method enhances the accuracy of real-time classification of EMG signals up to 95.5 percent for 800 msec signal length. The proposed postprocessing method achieves higher consistency compared with conventional majority voting and Bayesian fusion methods.
Guided flux motion: models and experiment

The article provides a brief overview of existing models describing guided flux motion. The first model was proposed by Niessen et al. This model works in the single-vortex approximation and provides qualitative explanation of the effect, but in most cases it gives an overestimated guiding angle. The stochastic model explains the experimentally observed effect of decreasing the guiding angle, the socalled slipping effect, by the influence of thermal fluctuations. However, the performed estimates show that thermal fluctuations should not be so significant. An alternative guiding model is the anisotropic pinning model. This model works in the critical state approximation, and the slipping effect is the result of the combined action of anisotropic pinning and vortex interaction. The predictions of all three models are verified by the example of a superconducting Nb-Ti tape containing about 6 vol. % α-Ti, which acts as strong pinning centers. Cutting samples at different angles to rolling allows one to control the direction of the driving force. It was found that when the standard criterion of the electric field is reached, the guiding angle varies significantly at different places along the sample, which is explained by the plastic mode of the vortex matter motion. In this case, the anisotropic pinning model semi-quantitatively predicts the direction angle averaged over the sample length. With an increase in the driving force, the vortex matter motion passes into an elastic mode, and the guiding angle becomes uniform along the sample.
The role of gentle algebras in higher homological algebra

Abstract We investigate the role of gentle algebras in higher homological algebra. In the first part of the paper, we show that if the module category of a gentle algebra Λ contains a d-cluster tilting subcategory for some d ≥ 2 {d\geq 2} , then Λ is a radical square zero Nakayama algebra. This gives a complete classification of weakly d-representation finite gentle algebras. In the second part, we use a geometric model of the derived category to prove a similar result in the triangulated setup. More precisely, we show that if 𝒟 b ⁡ ( Λ ) {\operatorname{\mathcal{D}}^{b}(\Lambda)} contains a d-cluster tilting subcategory that is closed under [ d ] {[d]} , then Λ is derived equivalent to an algebra of Dynkin type A. Furthermore, our approach gives a geometric characterization of all d-cluster tilting subcategories of 𝒟 b ⁡ ( Λ ) {\operatorname{\mathcal{D}}^{b}(\Lambda)} that are closed under [ d ] {[d]} .
Base Station Antenna Uptilt Optimization for Cellular-Connected Drone Corridors

Reliable wireless coverage in drone corridors is critical to enable a connected, safe, and secure airspace. To support beyond-visual-line-of-sight operations of aerial vehicles in a drone corridor, cellular base stations (BSs) can serve as a convenient infrastructure as they are widely deployed to provide seamless wireless coverage. However, antennas in the existing cellular networks are downtilted to optimally serve their ground users, which results in coverage holes at higher altitudes when they are used to serve drones. In this article, we consider the use of additional uptilted antennas at each cellular BS and optimize the uptilt angle to maximize the wireless coverage probability across a given drone corridor. Through numerical results, we characterize the optimal value of the antenna uptilt angle for a given antenna pattern as well as the minimum/maximum altitudes of the drone corridor.
Ab initio molecular orbital study of the structures and energies of neutral and charged bimolecular complexes of water with the hydrides AHn (A = nitrogen, oxygen, fluorine, phosphorus, sulfur, and chlorine)

Hartree-Fock 6-31G(d) structures for the neutral, positive ion, and negative ion bimolecular complexes of H/sub 2/O with the first- and second-row hydrides AH/sub n/ (AH/sub n/ = NH/sub 3/, OH/sub 2/, FH, PH/sub 3/, SH/sub 2/, and ClH) have been determined. All of the neutral complexes, the positive ion complexes of OH/sub 3//sup -/ with the hydrogen halides HF and HCl, and the first-row negative ion complexes exhibit conventional hydrogen-bonded structures with essentially linear hydrogen bonds, and directed lone pairs of electrons. The positive ion complexes of NH/sub 4//sup +/, OH/sub 3//sup +/, OH/sub 4//sup +/, and SH/sub 3//sup +/ with H/sub 2/O have linear hydrogen bonds, but the H/sub 2/O molecule is oriented so that its dipole moment vector lies along the intermolecular line, forming a directed-ion-dipole hydrogen bond. The complexes of PH/sub 2//sup -/, SH/sup -/, and Cl/sup -/ with H/sub 2/O have nonlinear hydrogen bonds. First-row complexes have greater stabilization energies and shorter intermolecular distances than the corresponding complexes of the second row. The neutral complexes of H/sub 2/O with PH/sub 3/ and SH/sub 2/ have nonassociative hydrogen bonds. Basis set and correlation effects on the computed hydrogen bond energies of these complexes have also been evaluated.
Post-explosion Evolution of Core-collapse Supernovae

We investigate the post-explosion phase in core-collapse supernovae with 2D hydrodynamical simulations and a simple neutrino treatment. The latter allows us to perform 46 simulations and follow the evolution of the 32 explosion models during several seconds. We present a broad study based on three progenitors (11.2, 15, and 27 M ⊙), different neutrino heating efficiencies, and various rotation rates. We show that the first seconds after shock revival determine the final explosion energy, remnant mass, and properties of ejected matter. Our results suggest that a continued mass accretion increases the explosion energy even at late times. We link the late-time mass accretion to initial conditions such as rotation strength and shock deformation at explosion time. Only some of our simulations develop a neutrino-driven wind (NDW) that survives for several seconds. This indicates that NDWs are not a standard feature expected after every successful explosion. Even if our neutrino treatment is simple, we estimate the nucleosynthesis of the exploding models for the 15 M ⊙ progenitor after correcting the neutrino energies and luminosities to get a more realistic electron fraction.