Miracle-dz/UniFAR-FTUs huggingface dataset:
Inflation and inflation uncertainty

This article examines whether higher inflation has been associated with greater inflation uncertainty in the United Kingdom during the post-war period, using various descriptive and econometric estimates of uncertainty. Though the results cannot establish conclusively whether there has been a causal link, they do suggest that the level of inflation and inflation uncertainty are positively correlated. If inflation uncertainty is costly, this provides a potential justification for directing policy at establishing and sustaining an environment of low inflation.
Effect of Financial Hedging on Capital Investments of Non-Financial Firms

Original Research Article This paper examines, in an empirical setting, the effect of financial hedging on investment decisions by non-financial firms. Using the total notional values of currency, interest rate and commodity derivatives as a measure of financial hedging by non-financial S&P 500 firms for a period of 1996-2000, we find that financial hedging reduces the sensitivity of investments to cash flow for the sample firms. This result holds even after controlling for several other variables including debt ratio, Tobin’s Q, and size. We find that hedging increases debt capacity and also hedging reduces the cash flow variability thus reducing the probability of financial distress. Consistent with past studies, We also find that investment is significantly negatively correlated with leverage and significantly positively correlated to internal cash flow and firm size.
Large-Scale Manual Validation of Bug Fixing Commits: A Fine-grained Analysis of Tangling

Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results: We estimate that between 17\% and 32\% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66\% to 87\%. We find that about 11\% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3\% to 47\% of data is noisy without manual untangling, depending on the use case. Conclusion: Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise.
PoseTrackReID: Dataset Description

Current datasets for video-based person re-identification (re-ID) do not include structural knowledge in form of human pose annotations for the persons of interest. Nonetheless, pose information is very helpful to disentangle useful feature information from background or occlusion noise. Especially real-world scenarios, such as surveillance, contain a lot of occlusions in human crowds or by obstacles. On the other hand, video-based person re-ID can benefit other tasks such as multi-person pose tracking in terms of robust feature matching. For that reason, we present PoseTrackReID, a large-scale dataset for multi-person pose tracking and video-based person re-ID. With PoseTrackReID, we want to bridge the gap between person re-ID and multi-person pose tracking. Additionally, this dataset provides a good benchmark for current state-of-the-art methods on multi-frame person re-ID.
Motion Generation Using Bilateral Control-Based Imitation Learning With Autoregressive Learning

Imitation learning has been studied as an efficient and high-performance method to generate robot motion. Specifically, bilateral control-based imitation learning has been proposed as a method of realizing fast motion. However, the learning approach of this method leads to the accumulation of prediction errors during the prediction process and may not generate desirable long-term behavior. Therefore, in this paper, we propose a method of autoregressive learning for bilateral control-based imitation learning to reduce the accumulation of prediction errors. A new neural network model for implementing autoregressive learning is also proposed. Three types of experiments are conducted to verify the effectiveness of the proposed method, where the method is shown to have improved performance over those of conventional approaches. Due to the structure and method of autoregressive learning employed by the developed model, the proposed method can generate desirable long-term motion for successful tasks and has a high generalization ability for environmental changes based on the human demonstrations of tasks.
Overview of the Ninth Dialog System Technology Challenge: DSTC9

This paper introduces the Ninth Dialog System Technology Challenge (DSTC-9). This edition of the DSTC focuses on applying end-to-end dialog technologies for four distinct tasks in dialog systems, namely, 1. Task-oriented dialog Modeling with unstructured knowledge access, 2. Multi-domain task-oriented dialog, 3. Interactive evaluation of dialog, and 4. Situated interactive multi-modal dialog. This paper describes the task definition, provided datasets, baselines and evaluation set-up for each track. We also summarize the results of the submitted systems to highlight the overall trends of the state-of-the-art technologies for the tasks.
The metabolic fingerprint of COVID-19 severity

Corona virus disease 2019 (COVID-19) has been associated with a wide range of divergent pathologies, and risk of severe disease is reported to be increased by a similarly broad range of co-morbidities. The present study investigated blood metabolites in order to elucidate how infection with severe acute respiratory syndrome coronavirus 2 can lead to such a variety of pathologies and what common ground they share. COVID-19 patient blood samples were taken at hospital admission in two Belgian patient cohorts, and a third cohort that included longitudinal samples was used for additional validation (total n=581). A total of 251 blood metabolite measures and ratios were assessed using nuclear magnetic resonance spectroscopy and tested for association to disease severity. In line with the varied effects of severe COVID-19, the range of severity-associated biomarkers was equally broad and included increased inflammatory markers (glycoprotein acetylation), amino acid concentrations (increased leucine and phenylalanine), increased lipoprotein particle concentrations (except those of very low density lipoprotein, VLDL), decreased cholesterol levels (except in large HDL and VLDL), increased triglyceride levels (only in IDL and LDL), fatty acid levels (decreased poly-unsaturated fatty acid, increased mono-unsaturated fatty acid) and decreased choline concentration, with association sizes comparable to those of routine clinical chemistry metrics of acute inflammation. Our results point to systemic metabolic biomarkers for COVID-19 severity that make strong targets for further fundamental research into its pathology (e.g. phenylalanine and omega-6 fatty acids).
Online Virtual Machine Allocation with Predictions

The cloud computing industry has grown rapidly over the last decade, and with this growth there is a significant increase in demand for compute resources. Demand is manifested in the form of Virtual Machine (VM) requests, which need to be assigned to physical machines in a way that minimizes resource fragmentation and efficiently utilizes the available machines. This problem can be modeled as a dynamic version of the bin packing problem with the objective of minimizing the total usage time of the bins (physical machines). Earlier works on dynamic bin packing assumed that no knowledge is available to the scheduler and later works studied models in which lifetime/duration of each "item" (VM in our context) is available to the scheduler. This extra information was shown to improve exponentially the achievable competitive ratio. Motivated by advances in Machine Learning that provide good estimates of workload characteristics, this paper studies the effect of having extra information regarding future (total) demand. In the cloud context, since demand is an aggregate over many VM requests, it can be predicted with high accuracy (e.g., using historical data). We show that the competitive factor can be dramatically improved by using this additional information; in some cases, we achieve constant competitiveness, or even a competitive factor that approaches 1. Along the way, we design new offline algorithms with improved approximation ratios for the dynamic bin-packing problem.
Bootstrap-Based Between-Study Heterogeneity Tests in Meta-Analysis

Abstract Meta-analysis combines pertinent information from existing studies to provide an overall estimate of population parameters/effect sizes, as well as to quantify and explain the differences between studies. However, testing between-study heterogeneity is one of the most challenging tasks in meta-analysis research. Existing methods for testing heterogeneity, such as the Q test and likelihood ratio (LR) test, have been criticized for their failure to control Type I error rate and/or failure to attain enough statistical power. Although better reference distribution approximations have been proposed in the literature, their application is limited. Additionally, when the interest is to test whether the size of the heterogeneity is larger than a specific level, existing methods are far from mature. To address these issues, we propose new heterogeneity tests. Specifically, we combine bootstrap methods with existing heterogeneity tests (i.e., the maximum LR test, the restricted maximum LR test, and the Q test) to overcome the reference distribution issue and denote them as B-ML-LRT, B-REML-LRT, and B-Q, respectively. Simulation studies were conducted to examine and compare the performance of the proposed methods with the regular LR test, the regular Q test, and the Kulinskaya’s improved Q test in both random- and mixed-effects meta-analyses. Based on the results of Type I error rates and statistical power, B-REML-LRT is recommended. Additionally, the improved Q test is also recommended when it is applicable. An R package is provided to facilitate the implementation of the proposed tests.
Development of Multi-Layer Fabrication Process for SFQ Large Scale Integrated Digital Circuits

We have developed a fabrication technology for superconducting integrated circuits with Nb-based Josephson junctions. The standard fabrication process with 10 mask levels includes 3 Nb superconducting layers and a Mo resistor layer. The influence of deposition parameters on film stress, electrical properties, and surface roughness were studied systematically. High quality Nb, Al, Mo, and SiO2 films were successfully deposited for the subsequent fabrication of circuits. The circuit fabrication started with the fabrication of Mo resistors with a target sheet resistance Rsh of 2 Ω, followed by the deposition of Nb/Al-AlOx/Nb trilayer Josephson-junction. The target critical current density Jc was set at 6 kA/cm2. Small-scale circuits such as our standard library cells have been successfully fabricated and tested, confirming the capability of our fabrication technology for superconducting integrated circuits.
The unequal-time matter power spectrum: impact on weak lensing observables

We investigate the impact of a common approximation of weak lensing power spectra: the use of single-epoch matter power spectra in integrals over redshift. We disentangle this from the closely connected Limber's approximation. We derive the unequal-time matter power spectrum at one-loop in standard perturbation theory and effective field theory to deal with non-linear physics. We compare these formalisms and conclude that the unequal-time power spectrum using effective field theory breaks for larger scales. As an alternative we introduce the midpoint approximation. We also provide, for the first time, a fitting function for the time evolution of the effective field theory counterterms based on the Quijote simulations. Then we compute the angular power spectrum using a range of approaches: the Limber approximation, and the geometric and midpoint approximations. We compare our results with the exact calculation at all angular scales using the unequal-time power spectrum. We use DES Y1 and LSST-like redshift distributions for our analysis. We find that the use of the Limber's approximation in weak lensing diverges from the exact calculation of the angular power spectrum on large-angle separations, ℓ < 10. Even though this deviation is of order 2% maximum for cosmic lensing, we find the biggest effect for galaxy clustering and galaxy-galaxy lensing. We show that not only is this true for upcoming galaxy surveys, but also for current data such as DES Y1. Finally, we make our pipeline and analysis publicly available as a Python package called unequalpy.
Noninertial effects on
 
 CP
 
 ‐violating systems

An analysis of accelerated kaon decays based on the Unruh effect shows a slight decrease in a CP-violation parameter for very high accelerations, as a consequence of the previously know increase in decay rates for non-inertial systems. Its consequences on the understanding of the relation between thermal and non-inertial phenomena are briefly discussed.
Quantitative measurement of IgG to SARS-CoV-2 proteins using ImmunoCAP

Background: Detailed understanding of the immune response to SARS-CoV-2, the cause of coronavirus disease 2019 (COVID-19), has been hampered by a lack of quantitative antibody assays. Objective: To develop a quantitative assay for IgG to SARS-CoV-2 proteins that could readily be implemented in clinical and research laboratories. Methods: The biotin-streptavidin technique was used to conjugate SARS-CoV-2 spike receptor-binding-domain (RBD) or nucleocapsid protein to the solid-phase of the ImmunoCAP resin. Plasma and serum samples from patients with COVID-19 (n=51) and samples from donors banked prior to the emergence of COVID-19 (n=109) were used in the assay. SARS-CoV-2 IgG levels were followed longitudinally in a subset of samples and were related to total IgG and IgG to reference antigens using an ImmunoCAP 250 platform. Results: Performance characteristics demonstrated 100% sensitivity and 99% specificity at a cut-off level of 2.5 g/mL for both SARS-CoV-2 proteins. Among 36 patients evaluated in a post-hospital follow-up clinic, median levels of IgG to spike-RBD and nucleocapsid were 34.7 g/mL (IQR 18-52) and 24.5 g/mL (IQR 9-59), respectively. Among 17 patients with longitudinal samples there was a wide variation in the magnitude of IgG responses, but generally the response to spike-RBD and to nucleocapsid occurred in parallel, with peak levels approaching 100 g/mL, or 1% of total IgG. Conclusions: We have described a quantitative assay to measure IgG to SARS-CoV-2 that could be used in clinical and research laboratories and implemented at scale. The assay can easily be adapted to measure IgG to novel antigens, has good performance characteristics and a read-out in standardized units.
FS-HGR: Few-Shot Learning for Hand Gesture Recognition via Electromyography

This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their widespread applications in human-machine interfaces. DNNs have been recently used for detecting the intended hand gesture through the processing of surface electromyogram (sEMG) signals. Objective: Although DNNs have shown superior accuracy compared to conventional methods when large amounts of data are available for training, their performance substantially decreases when data are limited. Collecting large datasets for training may be feasible in research laboratories, but it is not a practical approach for real-life applications. The main objective of this work is to design a modern DNN-based gesture detection model that relies on minimal training data while providing high accuracy. Methods: We propose the novel Few-Shot learning- Hand Gesture Recognition (FS-HGR) architecture. Few-shot learning is a variant of domain adaptation with the goal of inferring the required output based on just one or a few training observations. The proposed FS-HGR generalizes after seeing very few observations from each class by combining temporal convolutions with attention mechanisms. This allows the meta-learner to aggregate contextual information from experience and to pinpoint specific pieces of information within its available set of inputs. Data Source & Summary of Results: The performance of FS-HGR was tested on the second and fifth Ninapro databases, referred to as the DB2 and DB5, respectively. The DB2 consists of 50 gestures (rest included) from 40 healthy subjects. The Ninapro DB5 contains data from 10 healthy participants performing a total of 53 different gestures (rest included). The proposed approach for the Ninapro DB2 led to 85.94% classification accuracy on new repetitions with few-shot observation (5-way 5-shot), 81.29% accuracy on new subjects with few-shot observation (5-way 5-shot), and 73.36% accuracy on new gestures with few-shot observation (5-way 5-shot). Moreover, the proposed approach for the Ninapro DB5 led to 64.65% classification accuracy on new subjects with few-shot observation (5-way 5-shot).
COVID-PCD a participatory research study on the impact of COVID-19 in people with Primary Ciliary Dyskinesia

Introduction COVID-PCD is a participatory study initiated by people with PCD who have an essential vote in all stages of the research from the design of the study to the recruitment of participants, and interpretation and communication of the study results. COVID-PCD aims to collect epidemiological data in real time from people with PCD throughout the pandemic to describe incidence of COVID-19, symptoms, and course of disease; identify risk factors for prognosis; and assess experiences, wishes, and needs. Methods The study is advertised through patient support groups and participants register online on the study website (www.covid19pcd.ispm.ch). The study invites persons of any age from anywhere in the world with a suspected or confirmed PCD. A baseline questionnaire assesses details on PCD diagnosis, habitual symptoms, and COVID-19 episodes that occurred before study entry. Afterwards, participants receive a weekly follow-up questionnaire with questions on incident SARS-CoV-2 infections, current symptoms, social contact behaviour, and physical activity. Occasional thematic questionnaires are sent out focusing on emerging questions of interest chosen by people with PCD. In case of hospitalisation, patients or family members are asked to obtain a hospital report. Results are continuously analysed and summaries put online. Conclusion The study started recruitment on April 30, 2020, and 556 people with PCD completed the baseline questionnaire by November 2, 2020. The COVID-PCD study is a participatory study that follows people with PCD during the COVID-19 pandemic, helps to empower affected persons, and serves as a platform for communication between patients, physicians, and researchers.
Shared Prior Learning of Energy-Based Models for Image Reconstruction

We propose a novel learning-based framework for image reconstruction particularly designed for training without ground truth data, which has three major building blocks: energy-based learning, a patch-based Wasserstein loss functional, and shared prior learning. In energy-based learning, the parameters of an energy functional composed of a learned data fidelity term and a data-driven regularizer are computed in a mean-field optimal control problem. In the absence of ground truth data, we change the loss functional to a patch-based Wasserstein functional, in which local statistics of the output images are compared to uncorrupted reference patches. Finally, in shared prior learning, both aforementioned optimal control problems are optimized simultaneously with shared learned parameters of the regularizer to further enhance unsupervised image reconstruction. We derive several time discretization schemes of the gradient flow and verify their consistency in terms of Mosco convergence. In numerous numerical experiments, we demonstrate that the proposed method generates state-of-the-art results for various image reconstruction applications--even if no ground truth images are available for training.
An enhanced slope in the transmission spectrum of the hot Jupiter WASP-104b

We present the optical transmission spectrum of the hot Jupiter WASP-104b based on one transit observed by the blue and red channels of the DBSP spectrograph at the Palomar 200-inch telescope and 14 transits observed by the MuSCAT2 four-channel imager at the 1.52 m Telescopio Carlos Sanchez. We also analyse 45 additional K2 transits, after correcting for the flux contamination from a companion star. Together with the transit light curves acquired by DBSP and MuSCAT2, we are able to revise the system parameters and orbital ephemeris, confirming that no transit timing variations exist. Our DBSP and MuSCAT2 combined transmission spectrum reveals an enhanced slope at wavelengths shorter than 630 nm and suggests the presence of a cloud deck at longer wavelengths. While the Bayesian spectral retrieval analyses favour a hazy atmosphere, stellar spot contamination cannot be completely ruled out. Further evidence, from transmission spectroscopy and detailed characterisation of the host star's activity, is required to distinguish the physical origin of the enhanced slope.
Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration

We show that standard extragradient methods (i.e. mirror prox and dual extrapolation) recover optimal accelerated rates for first-order minimization of smooth convex functions. To obtain this result we provide fine-grained characterization of the convergence rates of extragradient methods for solving monotone variational inequalities in terms of a natural condition we call relative Lipschitzness. We further generalize this framework to handle local and randomized notions of relative Lipschitzness and thereby recover rates for box-constrained $\ell_\infty$ regression based on area convexity and complexity bounds achieved by accelerated (randomized) coordinate descent for smooth convex function minimization.
Λ_{c}^{+} Production and Baryon-to-Meson Ratios in pp and p-Pb Collisions at sqrt[s_{NN}]=5.02  TeV at the LHC.

The prompt production of the charm baryon Λ_{c}^{+} and the Λ_{c}^{+}/D^{0} production ratios were measured at midrapidity with the ALICE detector in pp and p-Pb collisions at sqrt[s_{NN}]=5.02 TeV. These new measurements show a clear decrease of the Λ_{c}^{+}/D^{0} ratio with increasing transverse momentum (p_{T}) in both collision systems in the range 2<p_{T}<12 GeV/c, exhibiting similarities with the light-flavor baryon-to-meson ratios p/π and Λ/K_{S}^{0}. At low p_{T}, predictions that include additional color-reconnection mechanisms beyond the leading-color approximation, assume the existence of additional higher-mass charm-baryon states, or include hadronization via coalescence can describe the data, while predictions driven by charm-quark fragmentation processes measured in e^{+}e^{-} and e^{-}p collisions significantly underestimate the data. The results presented in this Letter provide significant evidence that the established assumption of universality (colliding-system independence) of parton-to-hadron fragmentation is not sufficient to describe charm-baryon production in hadronic collisions at LHC energies.