Miracle-dz/UniFAR-FTUs huggingface dataset:
Chemical diffusion coefficient of electrons in nanostructured semiconductor electrodes and dye-sensitized solar cells

The properties and physical interpretation of the electron diffusion coefficient, Dn, observed in nanostructured semiconductors and dye-sensitized solar cells by small perturbation electrical and electrooptical techniques are investigated. The chemical diffusion coefficient is defined as the product of a thermodynamic factor (that accounts for the effect of nonideal statistics in a gradient of chemical potential) and a jump diffusion coefficient (that depends on average hopping distances and rates). The thermodynamic and kinetic similarities, as well as the differences, between interacting ions in the lattice and electrons in nanostructured semiconductors are discussed. From these considerations, the chemical diffusion coefficient of electrons for multiple trapping transport in nanoporous dye solar cells can be calculated indirectly, and the results are in agreement with the reduction of the kinetic-transport equations in the quasistatic approximation. The chemical diffusion coefficient in several models .
Discovery of CCS Velocity-coherent Substructures in the Taurus Molecular Cloud 1

We present the results of mapping observations toward a nearby starless filamentary cloud, the Taurus Molecular Cloud 1 (TMC-1), in the CCS ( , 45.379033 GHz) emission line, using the Nobeyama 45 m telescope. The map shows that the TMC-1 filament has a diameter of ∼0.1 pc and a length of ∼0.5 pc at a distance of 140 pc. The position–velocity diagrams of CCS clearly indicate the existence of velocity-coherent substructures in the filament. We identify 21 substructures that are coherent in the position–position–velocity space by eye. Most of the substructures are elongated along the major axis of the TMC-1 filament. The line densities of the subfilaments are close to the critical line density for the equilibrium (∼17 M☉ pc−1 for the excitation temperature of 10 K), suggesting that self-gravity should play an important role in the dynamics of the subfilaments.
The SAMI Galaxy Survey: mass–kinematics scaling relations

We use data from the Sydney-AAO Multi-object Integral-field spectroscopy (SAMI) Galaxy Survey to study the dynamical scaling relation between galaxy stellar mass M∗ and the general kinematic parameter $S_K = \sqrt{K V_{\rm rot}^2 + \sigma ^2}$ that combines rotation velocity Vrot and velocity dispersion σ. We show that the log M∗ – log SK relation: (1) is linear above limits set by properties of the samples and observations; (2) has slightly different slope when derived from stellar or gas kinematic measurements; (3) applies to both early-type and late-type galaxies and has smaller scatter than either the Tully–Fisher relation (log M∗ − log Vrot) for late types or the Faber–Jackson relation (log M∗ − log σ) for early types; and (4) has scatter that is only weakly sensitive to the value of K, with minimum scatter for K in the range 0.4 and 0.7. We compare SK to the aperture second moment (the ‘aperture velocity dispersion’) measured from the integrated spectrum within a 3-arcsecond radius aperture ($\sigma _{3^{\prime \prime }}$). We find that while SK and $\sigma _{3^{\prime \prime }}$ are in general tightly correlated, the log M∗ − log SK relation has less scatter than the $\log M_* - \log \sigma _{3^{\prime \prime }}$ relation.
Patent Exhaustion Regime and International Production Sharing: Winners and Losers?

On May 30, 2017, the Supreme Court held that the initial authorized sale of a patented item within or outside the U.S. “exhausts” all rights of the patentee to that item under the Patent Act. This decision goes against the Government’s position that a foreign sale authorized by the U.S. patentee should exhaust U.S. patent rights by default unless the patentee expressly reserved those rights. We examine how a shift in Northern regime from presumptive international exhaustion (PIE)---which presumes that a foreign sale triggers exhaustion but permits express-reservation of rights---to absolute international exhaustion (AIE)---which effectively precludes express-reservation of rights---impacts global welfare and its distribution. PIE subjects the firms that source in the South and sell in the North to the risk of a patent infringement lawsuit but allows the firms to price discriminate internationally. AIE allows the firms to avoid a patent infringement lawsuit but also precludes international price discrimination. Firms differ in the extent to which they engage in international fragmentation of their production process. We find that being engaged in the international fragmentation of production is neither a necessary nor a sufficient condition for a firm to prefer AIE over PIE. Nonetheless, it is still true that Northern firms with high cost share of the components that can be outsourced to the South tend to prefer AIE, while firms with low cost share of those components tend to prefer PIE. A shift from PIE to AIE increases fragmentation, lowers production costs, and eliminates North-South price discrimination in the final good. Global welfare rises. Welfare also rises in the North but falls in the South if the impact on firms' incentive for production fragmentation is weak, cost savings from sourcing in the South are low, or the Northern and Southern markets are dissimilar in size and average willingness to pay for each good.
The General Pair-based Weighting Loss for Deep Metric Learning

Deep metric learning aims at learning the distance metric between pair of samples, through the deep neural networks to extract the semantic feature embeddings where similar samples are close to each other while dissimilar samples are farther apart. A large amount of loss functions based on pair distances have been presented in the literature for guiding the training of deep metric learning. In this paper, we unify them in a general pair-based weighting loss function, where the minimizing objective loss is just the distances weighting of informative pairs. The general pair-based weighting loss includes two main aspects, (1) samples mining and (2) pairs weighting. Samples mining aims at selecting the informative positive and negative pair sets to exploit the structured relationship of samples in a mini-batch and also reduce the number of non-trivial pairs. Pair weighting aims at assigning different weights for different pairs according to the pair distances for discriminatively training the network. We detailedly review those existing pair-based losses inline with our general loss function, and explore some possible methods from the perspective of samples mining and pairs weighting. Finally, extensive experiments on three image retrieval datasets show that our general pair-based weighting loss obtains new state-of-the-art performance, demonstrating the effectiveness of the pair-based samples mining and pairs weighting for deep metric learning.
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks

We study the training and generalization of deep neural networks (DNNs) in the over-parameterized regime, where the network width (i.e., number of hidden nodes per layer) is much larger than the number of training data points. We show that, the expected $0$-$1$ loss of a wide enough ReLU network trained with stochastic gradient descent (SGD) and random initialization can be bounded by the training loss of a random feature model induced by the network gradient at initialization, which we call a neural tangent random feature (NTRF) model. For data distributions that can be classified by NTRF model with sufficiently small error, our result yields a generalization error bound in the order of $\tilde{\mathcal{O}}(n^{-1/2})$ that is independent of the network width. Our result is more general and sharper than many existing generalization error bounds for over-parameterized neural networks. In addition, we establish a strong connection between our generalization error bound and the neural tangent kernel (NTK) proposed in recent work.
Image generation of serotonin synthesis rates using alpha-methyltryptophan and PET.

PURPOSE We sought to create functional images of the serotonin (5-HT) synthesis rate obtained with alpha-[11C]methyl-L-tryptophan (alpha-MTrp) and PET and standardize them into the stereotaxic coordinate system. METHOD Dynamic PET scans were performed in 11 healthy subjects after an injection of alpha-MTrp. Results obtained by the Patlak plot and nonlinear least-squares methods using arterial plasma as input function were compared. The pixel-by-pixel calculation of functional images of 5-HT synthesis was done by the Patlak plot approach, after results were compared by two methods. Input function obtained by combining venous plasma and sinus radioactivities was also evaluated as an alternative to arterial input function. RESULTS There were no significant differences in 5-HT synthesis between the two calculation methods. The normalized venous input functions gave similar results as the arterial input function. The regional rates of 5-HT synthesis from functional images were not significantly different from those obtained by graphical plotting. CONCLUSION alpha-MTrp images could be converted into functional images representing 5-HT synthesis rates in the living brain, facilitating statistical comparison.
Regression with Conditional GAN

In recent years, impressive progress has been made in the design of implicit probabilistic models via Generative Adversarial Networks (GAN) and its extension, the Conditional GAN (CGAN). Excellent solutions have been demonstrated mostly in image processing applications which involve large, continuous output spaces. There is almost no application of these powerful tools to problems having small dimensional output spaces. Regression problems involving the inductive learning of a map, $y=f(x)$, $f:\mathbb{R} \rightarrow \mathbb{R}^m$, with $m$ small (e.g., $m=1$ or just a few) is one good case in point. The standard approach to solve regression problems is to probabilistically model the output $y$ as the sum of a mean function $m(x)$ and a noise variable $\sigma$; it is also usual to take the noise to be a Gaussian. These are done for convenience sake so that the likelihood of observed data is expressible in closed form. In the real world, on the other hand, stochasticity of the output is usually caused by missing or noisy input variables. Such a real world situation is best represented using an implicit model in which an extra noise vector, $z$ is included with $x$ as input. CGAN is naturally suited to design such implicit models. This paper makes the first step in this direction. Through several artificial and real world datasets, we demonstrate CGAN to be an effective approach for solving regression problems. We compare against Gaussian Processes and show that CGAN has excellent output likelihood properties, and hence, it models noise in a better way.
Generalized lattice Boltzmann method: Modeling, analysis, and elements

In this paper, we first present a unified framework for the modelling of generalized lattice Boltzmann method (GLBM). We then conduct a comparison of the four popular analysis methods (Chapman-Enskog analysis, Maxwell iteration, direct Taylor expansion and recurrence equations approaches) that have been used to obtain the macroscopic Navier-Stokes equations and nonlinear convection-diffusion equations from the GLBM, and show that from mathematical point of view, these four analysis methods are equivalent to each other. Finally, we give some elements that are needed in the implementation of the GLBM, and also find that some available LB models can be obtained from this GLBM.
Commensal Bacteria: An Emerging Player in Defense Against Respiratory Pathogens

A diverse community of trillions of commensal bacteria inhabits mucosal and epidermal surfaces in humans and plays an important role in defense against pathogens, including respiratory pathogens. Commensal bacteria act on the host's immune system to induce protective responses that prevent colonization and invasion by pathogens. On the other hand, these bacteria can directly inhibit the growth of respiratory pathogens by producing antimicrobial products/signals and competing for nutrients and adhesion sites. Such mechanisms preserve the niche for commensal bacteria and support the host in containing respiratory infections. Herein, we discuss current evidence on the role of commensal bacteria in conferring protection against respiratory pathogens and the underlying mechanisms by which these bacteria do so. A deeper knowledge of how commensal bacteria interact with the host and pathogens might provide new insights that are poised to aid in the development of vaccines and therapeutics that target infectious diseases.
Revised Description of Dust Diffusion and a New Instability Creating Multiple Rings in Protoplanetary Disks

Various instabilities have been proposed as a promising mechanism for accumulating dust. Moreover, some of them are expected to lead to multiple-ring structure formation and planetesimal formation in protoplanetary disks. In a turbulent gaseous disk, the growth of the instabilities and the dust accumulation are quenched by the turbulent diffusion of dust grains. The diffusion process has often been modeled by a diffusion term in the continuity equation for the dust density. The dust diffusion model, however, does not guarantee conservation of angular momentum in a disk. In this study, we first formulate equations that describe dust diffusion and also conserve the total angular momentum of a disk. Second, we perform a linear perturbation analysis on the secular gravitational instability (GI) using the equations. The results show that the secular GI is a monotonically growing mode, contrary to the result of previous analyses that found it overstable. We find that the overstability is caused by the nonconservation of the angular momentum. Third, we find a new axisymmetric instability due to the combination of dust–gas friction and turbulent gas viscosity, which we refer to as two-component viscous gravitational instability (TVGI). The most unstable wavelength of TVGI is comparable to or smaller than the gas scale height. TVGI accumulates dust grains efficiently, which indicates that TVGI is a promising mechanism for the formation of multiple-ring-like structures and planetesimals. Finally, we examine the validity of the ring formation via the secular GI and TVGI in the HL Tau disk and find both instabilities can create multiple rings whose width is about 10 au at orbital radii larger than 50 au.
Equipping Experts/Bandits with Long-term Memory

We propose the first reduction-based approach to obtaining long-term memory guarantees for online learning in the sense of Bousquet and Warmuth, 2002, by reducing the problem to achieving typical switching regret. Specifically, for the classical expert problem with $K$ actions and $T$ rounds, using our framework we develop various algorithms with a regret bound of order $\mathcal{O}(\sqrt{T(S\ln T + n \ln K)})$ compared to any sequence of experts with $S-1$ switches among $n \leq \min\{S, K\}$ distinct experts. In addition, by plugging specific adaptive algorithms into our framework we also achieve the best of both stochastic and adversarial environments simultaneously. This resolves an open problem of Warmuth and Koolen, 2014. Furthermore, we extend our results to the sparse multi-armed bandit setting and show both negative and positive results for long-term memory guarantees. As a side result, our lower bound also implies that sparse losses do not help improve the worst-case regret for contextual bandits, a sharp contrast with the non-contextual case.
A Unifying Framework for Education Policy Analysis∗

This paper develops a framework that allows us, for the first time, to compare the cost effectiveness of widely-discussed education policies, including incentive-based reforms. Central to the cost-effectiveness calculation, we propose an empirical strategy to separate the overall impact of teachers on student achievement into an incentive-varying component – labeled teacher effort – and incentive-invariant teacher ability while imposing minimal assumptions. The strategy draws on exogenous variation in the incentive strength of a well-known federal accountability scheme and rich administrative data covering all public school students in North Carolina over time. Our results from identifying contemporaneous teacher effort and ability separately indicate that a one standard deviation increase in ability is equivalent to 18 percent of a standard deviation increase in student test scores, versus a 5 percent increase in scores for a one standard deviation increase in effort. Further, the overall effect of teachers (measured by value-added) is increasing in accountability incentive strength. We then implement a structural estimation procedure to uncover the persistence of ability and effort, showing that effort affects future scores positively, though by less than ability. Combining these estimates with a model of school decision-making allows us to place incentive-based reforms on a common footing with a range of alternative education policies. For illustration, we show that incentive-based reforms can be more cost-effective than reforms that target teacher ability as a means of improving average student performance.
Light-induced anomalous Hall effect in massless Dirac fermion systems and topological insulators with dissipation

Employing the quantum Liouville equation with phenomenological dissipation, we investigate the transport properties of massless and massive Dirac fermion systems that mimics graphene and topological insulators, respectively. The massless Dirac fermion system does not show an intrinsic Hall effect, but it shows a Hall current under the presence of circularly-polarized laser fields as a nature of a optically-driven nonequilibrium state. Based on the microscopic analysis, we find that the light-induced Hall effect mainly originates from the imbalance of photocarrier distribution in momentum space although the emergent Floquet–Berry curvature also has a non-zero contribution. We further compute the Hall transport property of the massive Dirac fermion system with an intrinsic Hall effect in order to investigate the interplay of the intrinsic topological contribution and the extrinsic light-induced population contribution. As a result, we find that the contribution from the photocarrier population imbalance becomes significant in the strong field regime and it overcomes the intrinsic contribution. This finding clearly demonstrates that intrinsic transport properties of materials can be overwritten by external driving and may open a way to ultrafast optical-control of transport properties of materials.
Controlling systematics in ground-based CMB surveys with partial boresight rotation

Future CMB experiments will require exquisite control of systematics in order to constrain the B-mode polarization power spectrum. One class of systematics that requires careful study is instrumental systematics. The potential impact of such systematics is most readily understood by considering analysis pipelines based on pair differencing. In this case, any differential gain, pointing or beam ellipticity between the two detectors in a pair can result in intensity leakage into the B-mode spectrum, which needs to be controlled to a high precision due to the much greater magnitude of the total intensity signal as compared to the B-mode signal. One well-known way to suppress such systematics is through careful design of the scan-strategy, in particular making use of any capability to rotate the instrument about its pointing (boresight) direction. Here, we show that the combination of specific choices of such partial boresight rotation angles with redundancies present in the scan strategy is a powerful approach for suppressing systematic effects. This mitigation can be performed in analysis in advance of map-making and, in contrast to other approaches (e.g. deprojection or filtering), results in no signal loss. We demonstrate our approach explicitly with time ordered data simulations relevant to next-generation ground-based CMB experiments, using deep and wide scan strategies appropriate for experiments based in Chile. These simulations show a reduction of multiple orders of magnitude in the spurious B-mode signal arising from differential gain and differential pointing systematics.
Unbabel’s Submission to the WMT2019 APE Shared Task: BERT-Based Encoder-Decoder for Automatic Post-Editing

This paper describes Unbabel’s submission to the WMT2019 APE Shared Task for the English-German language pair. Following the recent rise of large, powerful, pre-trained models, we adapt the BERT pretrained model to perform Automatic Post-Editing in an encoder-decoder framework. Analogously to dual-encoder architectures we develop a BERT-based encoder-decoder (BED) model in which a single pretrained BERT encoder receives both the source src and machine translation mt strings. Furthermore, we explore a conservativeness factor to constrain the APE system to perform fewer edits. As the official results show, when trained on a weighted combination of in-domain and artificial training data, our BED system with the conservativeness penalty improves significantly the translations of a strong NMT system by -0.78 and +1.23 in terms of TER and BLEU, respectively. Finally, our submission achieves a new state-of-the-art, ex-aequo, in English-German APE of NMT.
Convergence of uniform triangulations under the Cardy embedding

We consider an embedding of planar maps into an equilateral triangle $\Delta$ which we call the Cardy embedding. The embedding is a discrete approximation of a conformal map based on percolation observables that are used in Smirnov's proof of Cardy's formula. Under the Cardy embedding, the planar map induces a metric and an area measure on $\Delta$ and a boundary measure on $\partial \Delta$. We prove that for uniformly sampled triangulations, the metric and the measures converge jointly in the scaling limit to the Brownian disk conformally embedded into $\Delta$ (i.e., to the $\sqrt{8/3}$-Liouville quantum gravity disk). As part of our proof, we prove scaling limit results for critical site percolation on the uniform triangulations, in a quenched sense. In particular, we establish the scaling limit of the percolation crossing probability for a uniformly sampled triangulation with four boundary marked points.
Exceptional Retreat of Kangerlussuaq Glacier, East Greenland, Between 2016 and 2018

Kangerdlugssuaq Glacier is one of Greenland’s largest tidewater outlet glaciers, accounting for approximately 5 % of all ice discharge from the Greenland ice sheet. In 2018 the Kangerdlussuaq ice front reached its most retreated position since observations began in 1932. We determine the relationship between retreat and: (i) ice velocity; and (ii) surface elevation change, to assess the impact of the retreat on the glacier trunk. Between 2016 and 2018 the glacier retreated ~5 km and brought the Kangerdlussuaq ice front into a major (~15 km long) overdeepening. Coincident with this retreat, the glacier thinned as a result of near-terminus acceleration in ice flow. The subglacial topography means that 2016-18 terminus recession is likely to trigger a series of feedbacks between retreat, thinning and glacier acceleration, leading to a rapid and high-magnitude increase in discharge and sea level rise contribution. Dynamic thinning may continue until the glacier reaches the upward sloping bed ~10 km inland of its current position. Incorporating these non-linear processes into prognostic models of the ice sheet to 2100 and beyond will be critical for accurate forecasting of the ice sheet’s contribution to sea level rise.