Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre-Analysis Plan: The Micro-foundations of Openness to Foreign Direct Investment: beyond the capital-labor divide

What explains variation in individual preferences towards foreign direct investment (FDI)? Previous research has focused on the divide between labor and capital but is inconclusive. While one strand of work nds that labor bene ts but domestic capital su ers from FDI (Pandya 2010; Pinto 2013; Owen 2018), another put that labor hurts (Owen 2015), while domestic capital makes a gain (Danzman 2020). We set out to adjudicate between these contrasting accounts by going beyond the labor-capital divide and integrating non-material factors into the picture. To do so, we eld a paired-pro le conjoint experiment to a representative sample of citizens in Brazil (# ≈ 2, 000), a large economy where FDI is politically salient, that requires individuals to compare various rm pro les di ering across multiple material and non-material attributes. We also collect detailed information on the individuals’ role in the economy and on their level of trust, nationalism, and chauvinism. To the best of our knowledge, this is the rst attempt at providing detailed microfoundations for the study of openness to FDI. Our expectation is that individual-level preferences are a function of both material incentives and political psychology, a nding that would add new and important dimensions to existing scholarship rooted on the Open Economy Politics (OEP) approach.
Predicting disordered regions driving phase separation of proteins under variable salt concentration

We investigate intrinsically disordered regions (IDRs) of phase separating proteins regarding their impact on liquid-liquid phase separation (LLPS) of the full protein. Our theoretical approach uses a mean-field theory that accounts for sequence-dependent electrostatic interactions via a random-phase approximation (RPA) and in addition allows for variable salt concentration for the condensed and dilute protein phases. The numerical solution of the complete phase diagrams together with the tie lines that we derive for this model system leaves two parameters to be determined by fitting experimental data on concentrations of all species involved in the system. For our comparisons, we focus on two proteins, PGL-3 and FUS, known to undergo LLPS. For PGL-3 we predict that its long IDR near the C-terminus promotes LLPS, which we validate through direct comparison with in vitro experimental results under the same physiological conditions. For the structurally more complex protein FUS the role of the low complexity (LC) domain in LLPS has been intensively studied. Apart from the LC domain we here investigate theoretically two IDRs, one near the N-terminus and another near the C-terminus. Our theoretical analysis of these domains predict that the IDR at the N-terminus (aa 1-285) is the main driver of LLPS of FUS by comparison to in vitro experiments of the full length protein under the same physiological temperature and salt conditions. SIGNIFICANCE Intrinsically disordered proteins are drivers of cellular liquid-liquid phase separation. However, it remains a challenge to directly predict the phase behaviour of a protein based on its primary sequence, and under physiological conditions. We present a random-phase approximation that allows for variable salt concentration and thus accounts for salt partitioning. We use this to link the sequence of the disordered regions with the behaviour of the complete protein through direct comparisons to in vitro phase-separation assays. In particular, for FUS we determine the exact region responsible for LLPS, weighting in a long-standing debate.
Two-Stage Adjustable Robust Optimization Applied to Optimal Power Flow Problems

In this work, we consider two-stage optimization problems with polynomial inequality and equality constraints under uncertainty. In the first stage, one needs to decide upon the values of a subset of optimization variables (control variables). In the second stage, the uncertainty is revealed and the rest of optimization variables (state variables) are set up as a solution to a known system of possibly non-linear equations. This type of problem occurs, for instance, in optimization for dynamical systems, such as electric power systems, gas or water networks. We propose an iterative algorithm to build a sequence of approximately robustly feasible solutions with an improving objective value under a semialgebraic uncertainty set. At each iteration, the algorithm optimizes over a subset of the feasible set and uses affine approximations of the second-stage equations while preserving the non-linearity of other constraints. We implement our approach for AC Optimal Power Flow and demonstrate the performance of our proposed method on Matpower instances. This paper focuses on problems with polynomial equalities, quadratic inequalities and ellipsoidal uncertainty, but the approach is suitable for more general setups.
Hydrothermal Liquefaction of Kraft Lignin-The influence of capping agents and residence time

In the context of exploring alternatives to replace fossil resources, lignin has been acknowledged as a renewable source of various aromatic compounds that have the potential of being precursors to chemicals as well as fuel additives. Originating from lignocellulosic biomass such as wood, lignin is an amorphous polymer with a high content of aromatic units and, in order to harness these units, it must be depolymerised. A major problem with current depolymerisation techniques, however, is that lignin repolymerises after being depolymerised, and forms an undesirable char fraction. The addition of capping agents and fine-tuning the reaction conditions can be used to mitigate such formation of char. This work has investigated the depolymerisation of kraft lignin in hydrothermal conditions under varying temperatures (290-335 °C), residence times (1-12 min) and charges of isopropanol (IPA/dry lignin, 0-4.9) which, aside from being a co-solvent, was hypothesised as acting as a capping agent. The influence of these reaction parameters on the molecular weights, yields and elemental compositions of the products was studied, along with changes in the molecular structure compared to the starting lignin. The product is a suspension of solid material, i.e. char, in an aqueous phase and thus any desired organic liquid phase requires extraction from the aqueous product. While the yield of char increased with temperature and residence time, it decreased with increasing isopropanol loading, suggesting that the isopropanol does in fact act as a capping agent. Most of the lignin forms a water-soluble fraction that precipitates when the aqueous product phase is acidified, thereby forming the precipitated solids fraction (PS). The components remaining dissolved after acidification of the product phase are known as acid soluble organics (ASO). A portion of the ASO fraction was aromatic monomers, with guaiacol dominating: this result was expected since the lignin was sourced from softwood. The amount of such monomers increased with residence time in the reactor. Molecular weight analyses showed a rapid depolymerisation of the lignin within 1 min of hydrothermal liquefaction (HTL) treatment via a significant decrease in the molecular weight of all product fractions: char, PS and ASO. Moreover, the carbon-oxygen inter-unit linkages were found to break in this timeframe as well. The repolymerisation reactions started to exceed depolymerisation between residence times of 4 and 12 min, causing the weight average molecular weight (Mw) to increase again. Although minimising the residence time allows the char yield and Mw to be kept low, more monomers were formed at longer residence times. This calls for careful tuning of the residence time in the HTL of kraft lignin.
COMMUNITY ANNOYANCE AND SLEEP DISTURBANCE: UPDATED CRITERIA FOR ASSESSING THE IMPACTS OF GENERAL TRANSPORTATION NOISE ON PEOPLE

The question of prediction of sleep disturbance and annoyance due to transportation noise has been addressed. Two sets of previously published data have been reanalyzed. This project was initiated as part of a long-term U.S. Air Force research program on the effects of aircraft noise on humans. It is concluded that day-night average sound level is still the most adequate noise descriptor for use in environmental impact analyses to assess the annoyance and overall impact of noise from general transportation, including civilian and military aircraft operations. A new logistic curve adopted in 1992 for general use by U.S. federal agencies, is recommended for use in environmental impact statements as the nominal relationship between day-night average sound level and the percentage of a general residential population predicted to be highly annoyed by the noise. A power curve, using A-weighted sound exposure level, is recommended for predicting nighttime sleep disturbance from general transportation noise.
Music As Medicine: A Historical Perspective and Review of Select Clinical Applications

Music is one of the oldest tools in medicine and has recently received considerable attention, allowing it to be studied with the same methodological rigor as other conventional interventions. In pain management, music provides distraction and cognitive imagery to aide relaxation. It also reduces pain perception by blocking pain impulses to the brain at the spinal cord and releasing endorphins, which simply help us feel good. Therapeutic music helps restore lost abilities, such as memory deficits in dementia, or speech and motor deficits following a brain injury or stroke. One of the fastest growing clinical applications of music therapy is with children with autism spectrum or other developmental disorders, helping improve self-expression, as well as social and shared communication, and perhaps reaching even less measurable goals such as improving self-esteem and quality of life. We know from neuroimaging research that brain circuitry involved in perception and processing of music closely overlaps with those regulating emotions, arousal, pleasure, and cognition. To a great extent, this explains how music can be used as a tool to influence and regulate these fundamental human functions. This paper provides a historical account of music in medicine and reviews select clinical applications. Contact Author: Talin Babikian, PhD Psychiatry and Biobehavioral Sciences UCLA School of Medicine 760 Westwood Plaza, Rm C8-746 Los Angeles, CA 90095 tbabikian@mednet.ucla. edu 310-825-0983
DAC-ML: Domain Adaptable Continuous Meta-Learning for Urban Dynamics Prediction

Given the underlying road network of an urban area, the problem of urban dynamics prediction aims to capture the patterns of urban dynamics and to forecast short-term urban traffic status continuously from the historical observations. This problem is of fundamental importance to urban traffic management, planning, and various business services. However, predicting urban dynamics is challenging due to the highly dynamic (i.e., varying across geographical locations and evolving over time) and uncertain (i.e., affected by unexpected factors) nature of urban traffic systems. Recent works adopt meta-learning approaches to capture irregular and rare patterns but make unrealistic assumptions such as single-domain uncertainties and explicit temporal task segmentation. In this paper, we solve the urban dynamics prediction problem from the Bayesian meta-learning perspective and propose a novel domain adaptable continuous meta-learning approach (DAC-ML) that does not require task segmentation. Trained on a sequence of spatial-temporal urban dynamics data, DAC-ML aims to detect and infer unobserved latent variations (from task and domain levels) and generalize well in a sequential prediction setting, where the underlying data generating process varies over time. Experimental results on three real-world datasets demonstrate that DAC-ML can outperform baselines in urban dynamics prediction, especially when obvious urban dynamics and temporal uncertainties are present.
A Fresh Look at Testing Vocabulary in EFL Context: Predictive Power of Multiple Intelligences

A classical model of human intelligence, Multiple Intelligences (MI) theory suitably provides EFL teachers with a conceptual framework for predicting the learners' performance on language tests. As an ex post facto research, this study incorporated both quantitative and qualitative data obtained from 130 Iranian EFL undergraduates into predicting their performance on an English standard test of vocabulary. The participants' scores on Multiple Intelligences Developmental Assessment Scales (MIDAS) questionnaire showed a matrix of correlation measures with three cloze, multiple choice and synonym items in Michigan English Language Assessment Battery (MELAB). The statistically significant correlation measures for linguistic intelligence and three sections in MELAB were incorporated into a standard multiple regression test, wherein an accountable degree of variance in vocabulary test scores could be predicted with participants' linguistic intelligence scores.
Flow cytometry-based quantification of targeted knock-in 2 events in human cell lines using a GPI-anchor biosynthesis 3 gene PIGP

1 Targeted knock-in supported by the CRISPR/Cas systems enables the insertion, 2 deletion, and substitution of genome sequences exactly as designed. Although this 3 technology is considered to have wide range of applications in life sciences, one of its 4 prerequisites for practical use is to improve the efficiency, precision, and specificity 5 achieved. To improve the efficiency of targeted knock-in, there first needs to be a 6 reporter system that permits simple and accurate monitoring of targeted knock-in events. 7 In this study, we created such a system using the PIGP gene, an autosomal gene 8 essential for GPI-anchor biosynthesis, as a reporter gene. We first deleted a PIGP allele 9 using Cas9 nucleases and then incorporated a truncating mutation into the other PIGP 10 allele in two near-diploid human cell lines. The resulting cell clones were used to 11 monitor the correction of the PIGP mutations by detecting GPI anchors distributed over 12 the cell membrane via flow cytometry. We confirmed the utility of these reporter clones 13 by performing targeted knock-in in these clones via a Cas9 nickase-based strategy 14 known as tandem paired nicking, as well as a common process using Cas9 nucleases, 15 and evaluating the efficiencies of the achieved targeted knock-in. We also leveraged 16 these reporter clones to test a modified procedure for tandem paired nicking, and 17 demonstrated a slight increase in the efficiency of targeted knock-in by the new 18 procedure. These data provide evidence for the utility of our PIGP -based assay system 19 to quantify the efficiency of targeted knock-in and thereby help improve the technology 20 of targeted knock-in.
Enhanced Neural Complexity is Achieved by Mutually Coordinated Embodied Social Interaction: A State-Space Analysis

Although cognitive complexity has been usually related to brain size or number of neurons, they are neither necessary nor sufficient conditions, since there are many species in nature showing that even with simple brains, they can exhibit unexpected levels of behavioral complexity. More recent approaches to cognitive science, such as enactive cognition, have been investigating social interaction in itself as part of an individual’s cognition. Recently, Candadai et al. (2019) have demonstrated through a minimal model that social interaction increases agents’ neural complexity and revealed that this cannot be achieved in isolation. In this paper, we first replicate the Candadai et al. (2019) model to analyze the state-space of the autonomous continuous-time recurrent neural networks of the interacting agents. Our results show that in terms of complexity, it is as simple as it can be, a single fixed-point attractor. Then, we proceed to ask whether, after loosening up the parameter constraints of this model, we will find more complexity in the state-space as there will be a broader variety of values in the parameters of neural controllers encoded in the genotype of each agent. Surprisingly, the state-space of this second approach leads to the same results, a single fixed-point attractor. Our findings, then, support the idea that cognitive complexity is mainly driven by the dynamics of social interaction rather than internal complexity.
Data-Driven Microseismic Event Localization: An Application to the Oklahoma Arkoma Basin Hydraulic Fracturing Data

The microseismic monitoring technique is widely applied to petroleum reservoirs to understand the process of hydraulic fracturing. Geophones continuously record the microseismic events triggered by fluid injection on the Earth’s surface or in monitoring wells. The microseismic event localization precision has a large impact on the performance of the technique. Deep learning has achieved significant progress in computer vision and natural language processing in recent years. We propose to use a deep convolutional neural network (CNN) to directly map the field records to their event locations. The biggest advantage of deep learning methods over conventional methods is that they can efficiently predict the characteristics of a huge amount of recorded data without human intervention. Thus, we use a CNN to predict the event location of field microseismic data that were recorded during a hydraulic fracturing process of a shale gas play in Oklahoma, the United States. We use synthetic data with extracted field noise from the records to train CNN. The synthetic training data allow us to produce the corresponding labels, and the extracted noise from the field data reduces the difference between the field and synthetic data. We use a correlation preprocessing step to avoid the need for event detection and picking of arrivals. We demonstrate that the proposed approach provides accurate microseismic event locations at a much faster speed than traditional imaging methods, such as time-reversal imaging. Comparison with an existing study on the same data is presented to evaluate the performance of the trained neural network.
Experimental and Numerical Investigation of Flexural Vibration Damping by Recycled Rubber Granulates

An investigation has been carried out in which recycled rubber granulates are added to damp rectangular tubes. A series of experiments have been carried out on the tube filled at different rates of filling. A model for calculating the displacement-force frequency responses has been derived by means of the Dynamic Stiffness Method (DSM) taken into account the assumption of internal resonance in quarter wavelength in granular material. Agreement between measured and calculated results is acceptable to permit estimation of complex wave-speed of granular material. Finally, we compare the quantified wave-speeds and their loss factors with experiments.
Household Income Inequality and Education in Marriage Market in Brazil: an empirical study

This paper aims to analyze the effects of changes in returns to education, educational composition and educational assortative mating in Brazilian household income inequality covering the period from 1992 to 2014. There is an evidence that the educational assortative mating in Brazil has decreased in the last 20 years whereas there was an increase in assortative mating in Brazil between higher educated couples. In addition, our results suggest that changes in marital sorting parameters had little effect on household income inequality whereas returns to education have played a major role. This effect can be explained by the fall in returns to education from 1992 to nowadays. Finally, if marriages were formed randomly across time the counterfactual Gini would be lower than the actual one from 1992 to 2014. All results are statistically significant. Applied Microeconomics JEL codes: J12, D31, I24, I21.
Feshbach resonances of large-mass-imbalance Er-Li mixtures

We report on the experimental observation of Feshbach resonances in large mass-imbalance mixtures of Erbium (Er) and Lithium (Li). All combinations between Er, Er and Li, Li are cooled to temperatures of a few microkelvin, partially by means of sympathetic cooling together with Ytterbium (Yb) as a third mixture component. The Er-Li inelastic interspecies collisional properties are studied for magnetic fields up to 680 G. In all cases resonant interspecies loss features, indicative of Feshbach resonances, have been observed. While most resonances have sub-Gauss widths, a few of them are broad and feature widths of several Gauss. Those broad resonances are a key to the realization of ultracold Er-Li quantum gas mixtures with tunable interactions.
Advantage of Machine Learning over Maximum Likelihood in Limited-Angle Low-Photon X-Ray Tomography

Limited-angle X-ray tomography reconstruction is an ill-conditioned inverse problem in general. Especially when the projection angles are limited and the measurements are taken in a photon-limited condition, reconstructions from classical algorithms such as filtered backprojection may lose fidelity and acquire artifacts due to the missing-cone problem. To obtain satisfactory reconstruction results, prior assumptions, such as total variation minimization and nonlocal image similarity, are usually incorporated within the reconstruction algorithm. In this work, we introduce deep neural networks to determine and apply a prior distribution in the reconstruction process. Our neural networks learn the prior directly from synthetic training samples. The neural nets thus obtain a prior distribution that is specific to the class of objects we are interested in reconstructing. In particular, we used deep generative models with 3D convolutional layers and 3D attention layers which are trained on 3D synthetic integrated circuit (IC) data from a model dubbed CircuitFaker. We demonstrate that, when the projection angles and photon budgets are limited, the priors from our deep generative models can dramatically improve the IC reconstruction quality on synthetic data compared with maximum likelihood estimation. Training the deep generative models with synthetic IC data from CircuitFaker illustrates the capabilities of the learned prior from machine learning. We expect that if the process were reproduced with experimental data, the advantage of the machine learning would persist. The advantages of machine learning in limited angle X-ray tomography may further enable applications in low-photon nanoscale imaging.
Polymatrix Competitive Gradient Descent

Many economic games and machine learning approaches can be cast as competitive optimization problems where multiple agents are minimizing their respective objective function, which depends on all agents' actions. While gradient descent is a reliable basic workhorse for single-agent optimization, it often leads to oscillation in competitive optimization. In this work we propose polymatrix competitive gradient descent (PCGD) as a method for solving general sum competitive optimization involving arbitrary numbers of agents. The updates of our method are obtained as the Nash equilibria of a local polymatrix approximation with a quadratic regularization, and can be computed efficiently by solving a linear system of equations. We prove local convergence of PCGD to stable fixed points for $n$-player general-sum games, and show that it does not require adapting the step size to the strength of the player-interactions. We use PCGD to optimize policies in multi-agent reinforcement learning and demonstrate its advantages in Snake, Markov soccer and an electricity market game. Agents trained by PCGD outperform agents trained with simultaneous gradient descent, symplectic gradient adjustment, and extragradient in Snake and Markov soccer games and on the electricity market game, PCGD trains faster than both simultaneous gradient descent and the extragradient method.