Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19

Abstract Dealing with structural breaks is an essential step in most empirical economic research. This is particularly true in panel data comprised of many cross-sectional units, which are all affected by major events. The COVID-19 pandemic has affected most sectors of the global economy; however, its impact on stock markets is still unclear. Most markets seem to have recovered while the pandemic is ongoing, suggesting that the relationship between stock returns and COVID-19 has been subject to structural break. It is therefore important to know if a structural break has occurred and, if it has, to infer the date of the break. Motivated by this last observation, the present article develops a new break detection toolbox that is applicable to different sized panels, easy to implement and robust to general forms of unobserved heterogeneity. The toolbox, which is the first of its kind, includes a structural change test, a break date estimator, and a break date confidence interval. Application to a panel covering 61 countries from January 3 to September 25, 2020, leads to the detection of a structural break that is dated to the first week of April. The effect of COVID-19 is negative before the break and zero thereafter, implying that while markets did react, the reaction was short-lived. A possible explanation is the quantitative easing programs announced by central banks all over the world in the second half of March.
The thesan project: predictions for multi-tracer line intensity mapping in the epoch of reionization

Line intensity mapping (LIM) is rapidly emerging as a powerful technique to study galaxy formation and cosmology in the high-redshift Universe. We present LIM estimates of select spectral lines originating from the interstellar medium (ISM) of galaxies and 21 cm emission from neutral hydrogen gas in the Universe using the large volume, high resolution thesan reionization simulations. A combination of sub-resolution photo-ionization modelling for H ii regions and Monte Carlo radiative transfer calculations is employed to estimate the dust-attenuated spectral energy distributions (SEDs) of high-redshift galaxies (z ≳ 5.5). We show that the derived photometric properties such as the ultraviolet (UV) luminosity function and the UV continuum slopes match observationally inferred values, demonstrating the accuracy of the SED modelling. We provide fits to the luminosity–star formation rate relation (L–SFR) for the brightest emission lines and find that important differences exist between the derived scaling relations and the widely used low-z ones because the interstellar medium of reionization era galaxies is generally less metal-enriched than in their low redshift counterparts. We use these relations to construct line intensity maps of nebular emission lines and cross-correlate with the 21 cm emission. Interestingly, the wavenumber at which the correlation switches sign (ktransition) depends heavily on the reionization model and to a lesser extent on the targeted emission line, which is consistent with the picture that ktransitionprobes the typical sizes of ionized regions. The derived scaling relations and intensity maps represent a timely state-of-the-art framework for forecasting and interpreting results from current and upcoming LIM experiments.
Mixed models and shrinkage estimation for balanced and unbalanced designs

Abstract The known connection between shrinkage estimation, empirical Bayes, and mixed effects models is explored and applied to balanced and unbalanced designs in which the responses are correlated. As an illustration, a mixed model is proposed for predicting the outcome of English Premier League games that takes into account both home and away team effects. Results based on empirical best linear unbiased predictors obtained from fitting mixed linear models are compared with fully Bayesian predictors that utilize prior information from the previous season.
Unitarily inequivalent quantum cosmological bouncing models

By quantising the background as well as the perturbations in a simple one fluid model, we show that there exists an ambiguity in the choice of relevant variables, potentially leading to incompatible observational physical predictions. In a classical or quantum inflationary background, the exact same canonical transformations lead to unique predictions, so the ambiguity we put forward demands a semiclassical background with a sufficiently strong departure from classical evolution. The latter condition happens to be satisfied in bouncing scenarios, which may thus be having predictability issues. Inflationary models could evade such a problem because of the monotonic behavior of their scale factor; they do, however, initiate from a singular state which bouncing scenarios aim at solving.
Towards Panoptic 3D Parsing for Single Image in the Wild

Performing single image holistic understanding and 3D reconstruction is a central task in computer vision. This paper presents an integrated system that performs dense scene labeling, object detection, instance segmentation, depth estimation, 3D shape reconstruction, and 3D layout estimation for indoor and outdoor scenes from a single RGB image. We name our system panoptic 3D parsing (Panoptic3D) in which panoptic segmentation ("stuff"segmentation and"things"detection/segmentation) with 3D reconstruction is performed. We design a stage-wise system, Panoptic3D (stage-wise), where a complete set of annotations is absent. Additionally, we present an end-to-end pipeline, Panoptic3D (end-to-end), trained on a synthetic dataset with a full set of annotations. We show results on both indoor (3D-FRONT) and outdoor (COCO and Cityscapes) scenes. Our proposed panoptic 3D parsing framework points to a promising direction in computer vision. Panoptic3D can be applied to a variety of applications, including autonomous driving, mapping, robotics, design, computer graphics, robotics, human-computer interaction, and augmented reality.
The Simons Observatory: Galactic Science Goals and Forecasts

Observing in six frequency bands from 27 to 280 GHz over a large sky area, the Simons Observatory (SO) is poised to address many questions in Galactic astrophysics in addition to its principal cosmological goals. In this work, we provide quantitative forecasts on astrophysical parameters of interest for a range of Galactic science cases. We find that SO can: constrain the frequency spectrum of polarized dust emission at a level of Δβ d ≲ 0.01 and thus test models of dust composition that predict that β d in polarization differs from that measured in total intensity; measure the correlation coefficient between polarized dust and synchrotron emission with a factor of two greater precision than current constraints; exclude the nonexistence of exo-Oort clouds at roughly 2. 9σ if the true fraction is similar to the detection rate of giant planets; map more than 850 molecular clouds with at least 50 independent polarization measurements at 1 pc resolution; detect or place upper limits on the polarization fractions of CO(2–1) emission and anomalous microwave emission at the 0.1% level in select regions; and measure the correlation coefficient between optical starlight polarization and microwave polarized dust emission in 1° patches for all lines of sight with N H ≳ 2 × 1020 cm−2. The goals and forecasts outlined here provide a roadmap for other microwave polarization experiments to expand their scientific scope via Milky Way astrophysics. 37 37 A supplement describing author contributions to this paper can be found at https://simonsobservatory.org/wp-content/uploads/2022/02/SO_GS_Contributions. pdf. A supplement describing author contributions to this paper can be found at https://simonsobservatory.org/wp-content/uploads/2022/02/SO_GS_Contributions. pdf.
A text autoencoder from transformer for fast encoding language representation

In recent years BERT shows apparent advantages and great potential in natural language processing tasks. However, both training and applying BERT requires intensive time and resources for computing contextual language representations, which hinders its universality and applicability. To overcome this bottleneck, we propose a deep bidirectional language model by using window masking mechanism at attention layer. This work computes contextual language representations without random masking as does in BERT and maintains the deep bidirectional architecture like BERT. To compute the same sentence representation, our method shows O(n) complexity less compared to other transformer-based models with O($n^2$). To further demonstrate its superiority, computing context language representations on CPU environments is conducted, by using the embeddings from the proposed method, logistic regression shows much higher accuracy in terms of SMS classification. Moverover, the proposed method also achieves significant higher performance in semantic similarity tasks.
A Modification of the Random Cutting Model

We propose a modification to the random destruction of graphs: given a finite network with a distinguished set of sources and targets, remove (cut) vertices at random, discarding components that do not contain a source node. We investigate the number of cuts required until all targets are removed, and the size of the remaining graph. This model interpolates between the random cutting model going back to Meir and Moon (J. Austral. Math. Soc.11, 1970) and site percolation. We prove several general results, including that the size of the remaining graph is a tight family of random variables for compatible sequences of expander-type graphs, and determine limiting distributions for binary caterpillar trees and complete binary trees.
Multipartite Intrinsic Non-Locality and Device-Independent Conference Key Agreement

In this work, we introduce multipartite intrinsic non-locality as a method for quantifying resources in the multipartite scenario of device-independent (DI) conference key agreement. We prove that multipartite intrinsic non-locality is additive, convex, and monotone under a class of free operations called local operations and common randomness. As one of our technical contributions, we establish a chain rule for two variants of multipartite mutual information, which we then use to prove that multipartite intrinsic non-locality is additive. This chain rule may be of independent interest in other contexts. All of these properties of multipartite intrinsic non-locality are helpful in establishing the main result of our paper: multipartite intrinsic non-locality is an upper bound on secret key rate in the general multipartite scenario of DI conference key agreement. We discuss various examples of DI conference key protocols and compare our upper bounds for these protocols with known lower bounds. Finally, we calculate upper bounds on recent experimental realizations of DI quantum key distribution.
Contrasting browsing in retail stores and e-surfing: Antecedents and consequences

Recently, some pieces of research have investigated the differences and/or similarities between consumer behavior online and offline (e.g., Filser, 2001a; Helme-Guizon, 2001) and merchandising online and offline (e.g., Volle 2000). In the same way, the aim of this paper is to give a better insight into the concepts of browsing in retail stores and e-surfing. First, we propose a clear definition of each concept. Then, we present the main antecedents and consequences of browsing in retail stores and e-surfing, contrast them and present some propositions. We also investigate if browsing and e-surfing may be considered as individual and/or group activities and if a browser is more likely to be also an e-surfer or the other way round. Finally, we pinpoint the importance of this stream of research from a managerial point of view and underline further research.
Intra- and interband excitations induced residue decay of the Bose polaron in a one-dimensional double-well

We investigate the polaronic properties of a single impurity immersed in a weakly interacting bosonic environment confined within a one-dimensional double-well potential using an exact diagonalization approach. We find that an increase of the impurity–bath coupling results in a vanishing residue, signifying the occurrence of the polaron orthogonality catastrophe. Asymptotic configurations of the systems’ ground state wave function in the strongly interacting regime are obtained by means of a Schmidt decomposition, which in turn accounts for the observed orthogonality catastrophe of the polaron. We exemplify that depending on the repulsion of the Bose gas, three distinct residue behaviors appear with respect to the impurity–bath coupling. These residue regimes are characterized by two critical values of the bosonic repulsion and originate from the interplay between the intra- and the interband excitations of the impurity. Moreover, they can be clearly distinguished in the corresponding species reduced density matrices with the latter revealing a phase separation on either the one- or the two-body level. The impact of the interspecies mass-imbalance on the impurity’s excitation processes is appreciated yielding an interaction shift of the residue regions. Our results explicate the interplay of intra- and interband excitation processes for the polaron generation in multiwell traps and for designing specific polaron entangled states motivating their exposure in current experiments.
Chiral quantum optics in broken-symmetry and topological photonic crystal waveguides

On-chip chiral quantum light-matter interfaces, which support directional interactions, provide a promising platform for efﬁcient spin-photon coupling, nonreciprocal photonic elements, and quantum logic architectures. We present full-wave three-dimensional calculations to quantify the performance of conventional and topological photonic crystal waveguides as chiral emitter-photon interfaces. Speciﬁcally, the ability of these structures to support and enhance directional interactions while suppressing subsequent backscattering losses is quantiﬁed. Broken symmetry waveguides, such as the nontopological glide-plane waveguide and topological bearded interface waveguide are found to act as efﬁcient chiral interfaces, with the topological waveguide modes allowing for operation at signiﬁcantly higher Purcell enhancement factors. Finally, although all structures suffer from backscattering losses due to fabrication imperfections, these are found to be smaller at high enhancement factors for the topological waveguide. These reduced losses occur because the optical mode is pushed away from the air-dielectric interfaces where scattering occurs, and not because of any topological protection. These results are important to the understanding of light-matter interactions in topological photonic crystal and design of efﬁcient, on-chip chiral quantum
Solving the sampling problem of the Sycamore quantum supremacy circuits

We study the problem of generating independent samples from the output distribution of Google’s Sycamore quantum circuits with a target ﬁdelity, which is believed to be beyond the reach of classical supercomputers and has been used to demonstrate quantum supremacy. We propose a new method to classically solve this problem by contracting the corresponding tensor network just once, and is massively more e ﬃ cient than existing methods in obtaining a large number of uncorrelated samples with a target ﬁdelity. For the Sycamore quantum supremacy circuit with 53 qubits and 20 cycles, we have generated one million uncorrelated bitstrings s which are sampled from a distribution (cid:98) P ( s ) = | (cid:98) ψ ( s ) | 2 , where the approximate state (cid:98) ψ has ﬁdelity F ≈ 0 . 0037. The whole computation has cost about 15 hours on a computational cluster with 512 GPUs. The obtained one million samples, the contraction code and contraction order are made public. If our algorithm could be implemented with high e ﬃ ciency on a modern supercomputer with ExaFLOPS performance, we estimate that ideally, the simulation would cost a few dozens of seconds, which is faster than Google’s quantum hardware.
The role of prophylactic antibiotics in nasal packing for epistaxis

Background: This study is to evaluate the necessity of prescribing prophylactic antibiotics for nasal packing in spontaneous epistaxis. There are few published papers of infective complications in such patients. Methods: This prospective study analysed 149 consecutive patients admitted to AL-Kindy teaching hospital with spontaneous, epistaxis, who underwent nasal packing over 2 years period . in the first year, 78 patients received prophylactic antibiotics , In the second year 71 patients were not given prophylatic antibiotics. Exclusion criteria included antibiotics prescribed for unrelated pathology and post-operative epistaxis. Signs and symptoms of acute otitis media, sinusitis and toxic shock syndrome were assessed using clinical examination and a questionnaire. Results: :Fourteen out of 149 patients experienced otalgia, mostly following posterior nasal packing. No patient in both groups had evidence of any infective complication. Conclusion: we do not recommend the use of prophylactic antibiotics for patients undergoing nasal packing for spontaineous epistaxis.
Modeling Techniques for Machine Learning Fairness: A Survey

Machine learning models are becoming pervasive in high-stakes applications. Despite their clear benefits in terms of performance, the models could show discrimination against minority groups and result in fairness issues in a decision-making process, leading to severe negative impacts on the individuals and the society. In recent years, various techniques have been developed to mitigate the unfairness for machine learning models. Among them, in-processing methods have drawn increasing attention from the community, where fairness is directly taken into consideration during model design to induce intrinsically fair models and fundamentally mitigate fairness issues in outputs and representations. In this survey, we review the current progress of in-processing fairness mitigation techniques. Based on where the fairness is achieved in the model, we categorize them into explicit and implicit methods, where the former directly incorporates fairness metrics in training objectives, and the latter focuses on refining latent representation learning. Finally, we conclude the survey with a discussion of the research challenges in this community to motivate future exploration.
The physics of partial discharges

This paper will be restricted to pulsed partial discharges (PDs) associated with streamers in gases. For this class of discharges, a classification scheme and a generic approach for their physical modelling will be presented and it will be shown that such models allow one to derive relations between measured PD data, defect characteristics, and design and test parameters as important tools for PD interpretation.
Evaluation of Triple Bottom Line Impacts of Resourceful Communities' Creating New Economies Fund

Resourceful Communities is a program of The Conservation Fund, a national nonprofit that promotes conservation and sustainable economic development. In 2001, Resourceful Communities established the Creating New Economies Fund (CNEF), a program to support triple bottom line community development work in North Carolina. The triple bottom line approach integrates sustainable economic development, environmental stewardship, and social justice to create solutions to challenges facing communities in North Carolina. Each year, CNEF awards small grants to partner organizations to develop and implement community-driven triple bottom line projects. Resourceful Communities has experienced difficulty in communicating the aggregate impacts of the partner projects that they support, which may be a result of inadequate reporting and evaluation tools, and is also confounded by the wide variety of projects they support. Resourceful Communities has requested that our team conduct a thorough evaluation of CNEF that will be used to clearly communicate their impacts, as well as provide recommendations to guide CNEF in improving the program and its reporting processes. We evaluated the impacts of the most recent three years of projects (2009-2011) supported by CNEF using a mixed-method approach, including surveys, interviews, site visits, and review of reports submitted by CNEF grantees. We distributed an online survey to grantee project leaders designed to gather information on the triple bottom line impacts of the projects. We conducted nine interviews with grantee project staff to better understand project impacts and gather grantees’ opinions on how CNEF could be improved. We performed three site visits to talk with project staff in person and to see some of the on-the-ground work being done by grantees. Finally, we reviewed all final reports submitted by the grantees to CNEF in order to gain information on projects that were not included in the survey or interviews. We used NVivo 10 qualitative analysis software to analyze qualitative data, and we used STATA and Excel to perform statistical analysis on quantitative data. We found that CNEF supported projects are contributing to triple bottom line sustainability in North Carolina in significant and widely varied ways. Further, we found the CNEF program fosters integration of the three components of the triple bottom line, community engagement, leveraged funding, and increased organizational capacity in the projects it supports. Based on our findings we were able to offer several recommendations to Resourceful Communities that might improve the CNEF program and its reporting practices.
On the Primal Feasibility in Dual Decomposition Methods Under Additive and Bounded Errors

With the unprecedented growth of signal processing and machine learning application domains, there has been a tremendous expansion of interest in distributed optimization methods to cope with the underlying large-scale problems. Nonetheless, inevitable system-specific challenges such as limited computational power, limited communication, latency requirements, measurement errors, and noises in wireless channels impose restrictions on the exactness of the underlying algorithms. Such restrictions have appealed to the exploration of algorithms' convergence behaviors under inexact settings. Despite the extensive research conducted in the area, it seems that the analysis of convergences of dual decomposition methods concerning primal optimality violations, together with dual optimality violations is less investigated. Here, we provide a systematic exposition of the convergence of feasible points in dual decomposition methods under inexact settings, for an important class of global consensus optimization problems. Convergences and the rate of convergences of the algorithms are mathematically substantiated, not only from a dual-domain standpoint but also from a primal-domain standpoint. Analytical results show that the algorithms converge to a neighborhood of optimality, the size of which depends on the level of underlying distortions.