Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimism and Bargaining Inefficiency

In a modified version of Rubinstein's bargaining game, two players expect the random arrival of a third party, from whom one of them will receive an interim disagreement payoff in every period until an agreement is finally reached. Each player thinks that his own probability of receiving the disagreement payoff is greater than that assessed by the other player; that is, they are mutually optimistic. We show that when the level of optimism is high and not very durable, equilibrium agreement is delayed until the uncertainty is fully resolved. The efficiency loss caused by such a delay remains substantial when the players are extremely patient.
A synergetic perspective on urban scaling, urban regulatory focus and their interrelations

By means of rich data, studies on urban scaling suggested that many urban properties scale with city size in universal ways. A recent study suggested an explanation why the behaviour of citizens in small and large cities differs qualitatively, by deriving the urban agents' behaviour from an extended version of Higgins’ regulatory focus theory regarding humans' motivational system. Based on several sets of laboratory experiments, this study demonstrated that urban context of large, fast-paced cities and that of small slow-paced cities encourage two distinctively different motivations and behaviours on the part of their inhabitants. What remains an open question following the above study, however, is the way these behavioural reactions are related to the dynamics of cities as complex, adaptive, self-organization systems. The aim of the present paper is to answer this open question. It does so from the theoretical perspective of Synergetics and its application to the domain of cities by means of synergetic inter-representation networks, information adaptation and their conjunction. From this conjunction, the paper suggests a theoretical interpretation associated with a mathematical model that links the theoretical framework to the empirical findings.
Endowment Assurance Products: Effectiveness of Risk-Minimizing Strategies under Model Risk

This paper analyzes and discusses the effects of model misspecification associated with both interest rate and mortality risk on the hedging decisions of insurance companies. We consider hedging strategies in different instruments (zero bonds) which are risk- (variance-) minimizing with respect to an assumed model. In this case, the associated expected costs and the variance of the costs are the same for all strategies. While the introduction of model risk, i.e. a deviation of assumed from true models, has the same effect on the expected costs, this is not true with respect to the variance. It turns out that the choice of hedging instruments has a crucial impact on the robustness of the strategies. In addition, the results of the paper can be used to emphasize the necessity to use a combined hedging model. In terms of robust hedging, a separate specification of interest rate model and mortality model is inconvenient, even in the case that interest rate and mortality are assumed to be independent.
The Evolution of International Output Differences (1970-2000): From Factors to Productivity

This article presents a group of exercises of level and growth decomposition of output per worker using cross-country data from 1970 to 2000. It is shown that in the early seventies factors of production (capital and education) were the main source of output dispersion across economies and that productivity variance was considerably smaller than in later years. Only after the mid-eighties did the prominence of productivity start to show up in the data, as the majority of the literature has found. The growth decomposition exercises show that the reversal of relative importance of productivity vis-à-vis factors is explained by the very good (bad) performance of productivity of fast- (slow-) growing economies. Although growth in the period, on average, is mostly due to factor accumulation, its variance is explained by productivity.
Innovation, Imitation and Competition

In a general equilibrium framework, it is known that imitation may actually promote innovation (Aghion et al., 1997). The same effect is demonstrated with a standard oligopoly model in which one firm has the ability to develop technologies while all other firms imitate and obtain a fraction of it for free. Competition is shown to dampen innovation, while imitation may stimulate it if imitation is strong and competition moderate. The findings have implications for policy toward intellectual property rights protection, as weak protection may promote rather than impede technology innovation.
Flexible Scheduling and the Gender Wage Gap

Using British data from the 2004 Workplace Employment Relations Survey, the author examines whether and how the relationship between schedule flexibility and wages differs by gender. Using a basic measure of whether the worker has discretion over the start and end times of work, men have more than twice the wage return to flexibility than do women, even within the same firm and detailed occupation. This difference cannot be explained by differences in household responsibilities or by differences in part time work by gender, but gender differences in job authority and autonomy do matter. Differences in the wage returns to this type of flexibility account for 10% of the gender wage gap, which suggests that understanding the underlying cause for the disparity in the returns to flexibility is important for understanding the policy implications of promoting flexible work arrangements.
Informality Traps

Abstract Despite large deregulation efforts, informal economic activity still represents a large share of GDP in many developing countries. In this paper we look at incentives to reduce informal activity when capitalists in the formal sector regulate entry. We consider a dual economy with a formal sector employing educated workers and an informal sector with unskilled workers. We show that high costs of education make labor migration and profits in the formal sector an increasing function of its size. Therefore, incentives to allow capital to be reallocated to the formal sector increase with the size of the formal economy, and unless the formal sector has reached a "critical mass" countries remain in a highly informal equilibrium. We conclude by reviewing policies that can push countries with large informal economies towards formalization.
From the State to the Street: The Segregated Loss of Childcare and the Institutional Origins of Organizational Deprivation

The inequitable distribution of social service organizations contributes to embedded systems of racial and economic inequality. But while the relationship between neighborhood disadvantage and organizational deprivation is well established, explanations for how these patterns originate are less so. Departing from the community and organization as analytical focal points, this article draws on a series of recent childcare center closures within Boston’s nonwealthy, majority-Black communities to elucidate the role of the state in processes of organizational deprivation. Informed by a multiyear, multimethods study, the author documents the disruption of one vital organizational cross-subsidization strategy brought on by the unanticipated intersection of two discrete public institutions. Findings reveal that, especially under conditions of limited welfare state expansion, understanding community-level patterns of deprivation requires elevating attention to institutional field-level dynamics.
Cost-Effective Cancer Screening: Bayesian Model Averaging with Two Sources of Variation

For medical testing, a popular measure of accuracy is the sensitivity or true positive rate TP/(TP+FN) to reduce the number of false negatives, which result in people who have the disease not receiving treatment. In contrast, there is a need for cost-effective tools that can achieve high precision, TP/(TP+FP), to identify a small fraction of the population with a high probability of having a disease, and thus avoid the cost of testing a large population. This paper explores how Bayesian model averaging can achieve better precision in a novel way. The task is predicting who has mesothelioma, a lung cancer linked with asbestos.
Expression of cluster differentiation-44 stem cell marker in grades of oral epithelial dysplasia: A preliminary study

Introduction: Oral cancer is one among the alarming diseases related to oral cavity. Its prevalence and incidence have increased in many folds, in the past decade. This has led the investigators to find the preliminary stages and related early evaluating methods to restrain it. Few clinical lesions such as leukoplakia, erythroplakia, oral submucous fibrosis and lichen planus reflected malignant changes. These premalignant disorders provided scope to assess the underlying cellular and molecular events, which shall be helpful in early detection, aggressiveness and prognosis of the patient. Materials and Methods: Forty formalin fixed, paraffin embedded blocks were utilized and evenly subdivided into Group I – control tissue, Group II – mild epithelial dysplasia, Group III – moderate epithelial dysplasia and Group IV – severe epithelial dysplasia. The study group was categorized based on the WHO classification of dysplasia 2005. Routine staining was performed to reconfirm the diagnosis of all the samples. Simultaneously, immunohistochemical staining was done with cluster differentiation-44 (CD44) antibody. Positive cells were counted on 10 representative fields with a minimum of 100 cells per field using ×20. Statistical Analysis: Comparison of four groups with respective to number of positive cells was done using Kruskal–Wallis ANOVA test. Pair-wise comparison of three grades of oral epithelial dysplasia and the controls was done using Mann–Whitney U test. Results: The mean of Group I is 745.50, Group II is 665.20, Group III is 530.10 and Group IV is 322.90. A statistically significant P = 0.00001 was ascertained on comparison of the mean between the groups. Conclusion: CD44, a cell membrane marker could help in cell adhesion and cell-cell interactions. Loss of CD44 expression enhances the binding of the growth factors with their principle receptors that enhances the cellular proliferation. It can be used as a prognostic marker for identifying the rate of malignant transformation in these disorders.
Steady state analysis of a multi-recombinative meta-ES on a conically constrained problem with comparison to σSA and CSA

This paper concerns the theoretical analysis of a multi-recombinative meta-ES with repair by projection applied to a conically constrained problem. Using theoretical results for the mean value dynamics and steady state considerations of the inner ES, approximate closed-form expressions for the mean value dynamics and the steady state behavior of the outer ES are derived. The approximation quality is shown by comparison with real meta-ES runs using isolation periods larger than one. The theoretical results are compared to known theoretical results of the multi-recombinative ES with σ-Self-Adaptation and Cumulative Step-Size adaptation. It is shown that the meta-ES achieves the largest steady state progress for the considered problem at the cost of twice the function evaluations compared to the other variants.
A Less Biased Evaluation of Out-of-distribution Sample Detectors

In the real world, a learning system could receive an input that is unlike anything it has seen during training. Unfortunately, out-of-distribution samples can lead to unpredictable behaviour. We need to know whether any given input belongs to the population distribution of the training/evaluation data to prevent unpredictable behaviour in deployed systems. A recent surge of interest in this problem has led to the development of sophisticated techniques in the deep learning literature. However, due to the absence of a standard problem definition or an exhaustive evaluation, it is not evident if we can rely on these methods. What makes this problem different from a typical supervised learning setting is that the distribution of outliers used in training may not be the same as the distribution of outliers encountered in the application. Classical approaches that learn inliers vs. outliers with only two datasets can yield optimistic results. We introduce OD-test, a three-dataset evaluation scheme as a more reliable strategy to assess progress on this problem. We present an exhaustive evaluation of a broad set of methods from related areas on image classification tasks. Contrary to the existing results, we show that for realistic applications of high-dimensional images the previous techniques have low accuracy and are not reliable in practice.
AMNet: Convolutional Neural Network embeded with Attention Mechanism for Semantic Segmentation

DeepLabv3+ is one of the most accurate algorithms in semantic segmentation. CBAM is an attention mechanism proposed to improve the performance of obect detection model which can be used in a convolutional neural network. Given an intermediate feature map, CBAM sequentially infers attention maps along two separate dimensions, channel and spatial, then the attention maps are multiplied to the input feature map for adaptive feature refinement. In the image segmentation tasks, in order to achieve the goal of enhancing feature representation and improving segmentation accuracy without extra overheads. In this paper, we proposed AMNet which is an end-to-end semantic segmentation network based on DeepLabv3+ which is embeded with CBAM. Further, CBAM activates when the input image passes through CNN.Channel attention module in CBAM focues on 'what' is meaningful given an input image and spatial attention module focus on 'where'. Our network acheives 77.66% mIoU on the PASCAL VOC2012, which is a 2.73% better mIoU than DeepLabv3+ with 6 batchsize using only one single Nvidia 2080 GPU. Beyond that, for getting a faster segmentation model, we also embed the attention mechanism into ENet, one of the fastest lightweight networks. After our evaluation on the Cityscapes dataset, we got a better performance in the case of fast training speed. The feasibility that attention mechanism can be integrated into semantic segmentaion network is proved.
WISE J072003.20-084651.2B is a Massive T Dwarf

We present individual dynamical masses for the nearby M9.5+T5.5 binary WISE J072003.20−084651.2AB, a.k. a. Scholz’s star. Combining high-precision Canada–France–Hawaii Telescope/WIRCam photocenter astrometry and Keck adaptive optics resolved imaging, we measure the first high-quality parallactic distance ( pc) and orbit ( yr period) for this system composed of a low-mass star and brown dwarf. We find a moderately eccentric orbit ( ), incompatible with previous work based on less data, and dynamical masses of 99 ± 6 and 66 ± 4 for the two components. The primary mass is marginally inconsistent (2.1σ) with the empirical mass–magnitude–metallicity relation and models of main-sequence stars. The relatively high mass of the cold ( K) brown dwarf companion indicates an age older than a few gigayears, in accord with age estimates for the primary star, and is consistent with our recent estimate of ≈70 for the stellar/substellar boundary among the field population. Our improved parallax and proper motion, as well as an orbit-corrected system velocity, improve the accuracy of the system’s close encounter with the solar system by an order of magnitude. WISE J0720−0846AB passed within 68.7 ± 2.0 kau of the Sun 80.5 ± 0.7 kyr ago, passing through the outer Oort cloud where comets can have stable orbits.
Decouplings for Surfaces of Zero Curvature

We extend the $l^2(L^p)$ decoupling theorem of Bourgain-Demeter to the full class of developable surfaces in $\mathbb{R}^3$. This completes the $l^2$ decoupling theory of the zero Gaussian curvature surfaces that lack planar (or umbilic) points. Of central interest to our study is the tangent surface associated to the moment curve.
Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation

Estimating the 6D pose of objects using only RGB images remains challenging because of problems such as occlusion and symmetries. It is also difficult to construct 3D models with precise texture without expert knowledge or specialized scanning devices. To address these problems, we propose a novel pose estimation method, Pix2Pose, that predicts the 3D coordinates of each object pixel without textured models. An auto-encoder architecture is designed to estimate the 3D coordinates and expected errors per pixel. These pixel-wise predictions are then used in multiple stages to form 2D-3D correspondences to directly compute poses with the PnP algorithm with RANSAC iterations. Our method is robust to occlusion by leveraging recent achievements in generative adversarial training to precisely recover occluded parts. Furthermore, a novel loss function, the transformer loss, is proposed to handle symmetric objects by guiding predictions to the closest symmetric pose. Evaluations on three different benchmark datasets containing symmetric and occluded objects show our method outperforms the state of the art using only RGB images.
Noncommutative Cartan \(\mathrm {C}^*\)-subalgebras

We characterise Exel's noncommutative Cartan subalgebras in several ways using uniqueness of conditional expectations, relative commutants, or purely outer inverse semigroup actions. We describe in which sense the crossed product decomposition for a noncommutative Cartan subalgebra is unique. We relate the property of being a noncommutative Cartan subalgebra to aperiodic inclusions and effectivity of dual groupoids. In particular, we extend Renault's characterisation of commutative Cartan subalgebras.
Resolving 3D Human Pose Ambiguities With 3D Scene Constraints

To understand and analyze human behavior, we need to capture humans moving in, and interacting with, the world. Most existing methods perform 3D human pose estimation without explicitly considering the scene. We observe however that the world constrains the body and vice-versa. To motivate this, we show that current 3D human pose estimation methods produce results that are not consistent with the 3D scene. Our key contribution is to exploit static 3D scene structure to better estimate human pose from monocular images. The method enforces Proximal Relationships with Object eXclusion and is called PROX. To test this, we collect a new dataset composed of 12 different 3D scenes and RGB sequences of 20 subjects moving in and interacting with the scenes. We represent human pose using the 3D human body model SMPL-X and extend SMPLify-X to estimate body pose using scene constraints. We make use of the 3D scene information by formulating two main constraints. The inter-penetration constraint penalizes intersection between the body model and the surrounding 3D scene. The contact constraint encourages specific parts of the body to be in contact with scene surfaces if they are close enough in distance and orientation. For quantitative evaluation we capture a separate dataset with 180 RGB frames in which the ground-truth body pose is estimated using a motion capture system. We show quantitatively that introducing scene constraints significantly reduces 3D joint error and vertex error. Our code and data are available for research at https://prox.is. tue.mpg. de.
Oscillating scalar fields and the Hubble tension: A resolution with novel signatures

We present a detailed investigation of a subdominant oscillating scalar field [“early dark energy” (EDE)] in the context of resolving the Hubble tension. Consistent with earlier work, but without relying on fluid approximations, we find that a scalar field frozen due to Hubble friction until log10(zc)∼3.5, reaching ρEDE(zc)/ρtot∼10% and diluting faster than matter afterwards, can bring cosmic microwave background (CMB), baryonic acoustic oscillations, supernovae luminosity distances, and the late-time estimate of the Hubble constant from the SH0ES Collaboration into agreement. A scalar field potential that scales as V(ϕ)∝ϕ2n with 2≲n≲3.4 around the minimum is preferred at the 68% confidence level, and the Planck polarization places additional constraints on the dynamics of perturbations in the scalar field. In particular, the data prefer a potential that flattens at large field displacements. A Markov-chain Monte Carlo analysis of mock data shows that the next-generation CMB observations (i.e., CMB-S4) can unambiguously detect the presence of the EDE at a very high significance. This projected sensitivity to the EDE dynamics is mainly driven by improved measurements of the E-mode polarization. We also explore new observational signatures of EDE scalar field dynamics: (i) We find that depending on the strength of the tensor-to-scalar ratio, the presence of the EDE might imply the existence of isocurvature perturbations in the CMB. (ii) We show that a strikingly rapid, scale-dependent growth of EDE field perturbations can result from parametric resonance driven by the anharmonic oscillating field for n≈2. This instability and ensuing potentially nonlinear, spatially inhomogeneous, dynamics may provide unique signatures of this scenario.
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution

By benefiting from perceptual losses, recent studies have improved significantly the performance of the super-resolution task, where a high-resolution image is resolved from its low-resolution counterpart. Although such objective functions generate near-photorealistic results, their capability is limited, since they estimate the reconstruction error for an entire image in the same way, without considering any semantic information. In this paper, we propose a novel method to benefit from perceptual loss in a more objective way. We optimize a deep network-based decoder with a targeted objective function that penalizes images at different semantic levels using the corresponding terms. In particular, the proposed method leverages our proposed OBB (Object, Background and Boundary) labels, generated from segmentation labels, to estimate a suitable perceptual loss for boundaries, while considering texture similarity for backgrounds. We show that our proposed approach results in more realistic textures and sharper edges, and outperforms other state-of-the-art algorithms in terms of both qualitative results on standard benchmarks and results of extensive user studies.
The gluon Sivers function and its process dependence from RHIC data

We present a phenomenological analysis of available data on single spin asymmetries for pion and $D$ meson production in proton-proton collisions at RHIC within the color gauge invariant generalized parton model (CGI-GPM), which includes initial (ISIs) and final (FSIs) state interactions in a transverse momentum dependent formalism. This study allows us, for the first time, to put preliminary constraints on the two independent gluon Sivers functions entering the model. We also present a comparison with the simpler generalized parton model (without ISIs and FSIs).