Miracle-dz/UniFAR-FTUs huggingface dataset:
RNA-extraction-free diagnostic method to detect SARS-CoV-2: an assessment from two states, India

Abstract With increasing demand for large numbers of testing during the coronavirus disease 2019 pandemic, alternative protocols were developed with shortened turn-around time. We evaluated the performance of such a protocol wherein 1138 consecutive clinic attendees were enrolled; 584 and 554 respectively from two independent study sites in the cities of Pune and Kolkata. Paired nasopharyngeal and oropharyngeal swabs were tested by using both reference and index methods in a blinded fashion. Prior to conducting real-time polymerase chain reaction, swabs collected in viral transport medium (VTM) were processed for RNA extraction (reference method) and swabs collected in a dry tube without VTM were incubated in Tris–EDTA–proteinase K buffer for 30 min and heat-inactivated at 98 °C for 6 min (index method). Overall sensitivity and specificity of the index method were 78.9% (95% confidence interval (CI) 71–86) and 99% (95% CI 98–99.6), respectively. Agreement between the index and reference method was 96.8% (k = 0.83, s.e. = 0.03). The reference method exhibited an enhanced detection of viral genes (E, N and RNA-dependent RNA polymerase) with lower Ct values compared to the index method. The index method can be used for detecting severe acute respiratory syndrome corona virus-2 infection with an appropriately chosen primer–probe set and heat treatment approach in pressing time; low sensitivity constrains its potential wider use.
E uropean X tramile C entre of A frican S tudies (EXCAS) EXCAS Working Paper

Motivated by the momentous rise in ICT diffusion, the implementation of the African Continental Free Trade Area agreement, and the expected rebound of foreign direct investment inflow to Africa from 2022, this study examines the joint effects of industrialisation and ICT diffusion on resource mobilisation in Africa. To this end, we use data on 42 African countries for the period 1996 – 2020 for the analysis. First, we provide evidence robust to several specifications from the dynamic system GMM to show that although unconditionally both industrialisation and ICT diffusion enhance (i) goods and services tax (GST), and (ii) profits, corporate and income tax (PCIT) mobilisation efforts in Africa, the effects of the former are rather remarkable in the presence of the latter. Particularly, the results show that, while ICTs amplify the effect of industrialisation on GST, only ICT usage and ICT skills matter for PCIT. Second, the study unveils ICT thresholds for complementary policies. Accordingly, industrialisation and ICTs are necessary and sufficient conditions for tax revenue mobilisation only below some ICT thresholds. Above these ICT thresholds, complementary policies are needed to maintain the overall positive incidence on tax revenue mobilisation. Policy recommendations are provided in the end.
Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System

Algorithms (in some form) are already widely used in the criminal justice system. We draw lessons from this experience for what is to come for the rest of society as machine learning diffuses. We find economists and other social scientists have a key role to play in shaping the impact of algorithms, in part through improving the tools used to build them.
The Equity Share Cycle

Standard financial portfolio theory recommends increasing the equity share of the portfolio as the equity premium rises. On the other hand, purely mechanically high stock prices imply low expected returns. Motivated by these opposite predictions I use data from 16 developed economies between 1873 to 2015 to study the composition of aggregate household wealth portfolio and document that in most countries the equity share has moved in very low-frequency cycles, which take decades to mean revert. I document a negative relationship between equity share and subsequent stock market returns with equity share having significant predictive power over future returns while outperforming the historical mean and traditional predictors in- and out-of-sample. I derive two new decompositions based on present value identities that help to understand these results in a framework of multiple assets classes. Furthermore, a high level of equity share is associated with a higher probability of a financial crisis. Standard asset pricing models have difficulty explaining the presented facts but a behavioral model where households have extrapolative expectations driven by unobserved market sentiment can offer a solution.
Risk as Excuses to Postpone Effort-Provision

The future is inherently more uncertain than the present. Existing literature has shown that people act impatiently or in a time-inconsistent manner at least partly because of their aversion to future risk. While such intuition could be fully captured by standard economic models, the current paper proposes a novel mechanism in which risk preferences interact with intertemporal choices---individuals may use risk as an excuse to postpone effort-provision. In a pre-registered laboratory experiment, participants evaluate risky prospects of workloads for today and those for the next week. Compared to when the valuation decisions involve no intertemporal tradeoff, when they do, individuals act more averse to present risk and less averse to future risk. In other words, they take more present-oriented actions under the veil of risks. Such patterns are more salient for men, for more forward-looking individuals, and for those who do not answer all three questions correctly in the cognitive reflection test (CRT). However, we do not find similar patterns when outcomes are monetary payoffs. By contrast, using a wiggle room task, we are able to find evidence supporting excused-driven information avoidance for both effort and money.
Particle Size Distributions of Airborne Particulate Matter in a Ferrosilicon Smelter

Dust and fume emissions are a prominent issue in the ferroalloy industry and relevant to both indoor air quality, occupational exposure and outdoor air quality and other environmental effects. Exposure assessment with respect to respirable particles and “total dust” have been demanded from the authorities for years. Recently the focus has widened and exposure assessment focusing on ultrafine particles is in demand. In this study, ultrafine particles are studied in a ferrosilicon smelter. In order to understand the prevalence of ultrafine particles in the workplace environment, five different locations in the production hall were selected and the number concentration and the particle size distributions were investigated by use of stationary instruments. The worker attendance at each location was logged, so that the exposure at each location could be evaluated in accordance to attendance time. Number concentrations for the time periods where workers were present could be calculated. The locations selected were: tapping area, casting area, ladle transport corridor, control room and electrode-weld-on”. The measurement equipment used was a Nanoscan SMPS from TSI, measuring the particle size range 10-420 nm. The dominating size in the tapping areas is 205.4 nm for both furnaces and one SAF has an additional peak is at 11.5-15.4 nm. In the ladle transport area, the dominating particle sizes are 15.4 nm, 27.4 nm and 154 nm. The casting area has a bimodal size distribution and the two peaks are at 15,4 nm and 154 nm. The electrode welding area is very different from the other areas investigated here. The concentrations are significantly higher and the dominating size is 205.4 nm, but with substantial increased contributions in the range 86-365 nm. This paper discusses the findings and explores possible explanations behind the particle size distributions with support in the literature. Different dust and fume sources present in a smelter are discussed.
Leveraging Value and Environmental Gains Through Integrated Steel- and Ferroalloy Plant Slag Cleaning

There exists a strong business case to incorporate a pyrometallurgical Slag Cleaning Furnace (SCF) into the process-chain of large ferrochrome producing sites, or into an integrated steel plant where ferroalloy production is in close proximity to the converters. Recent studies into the design and layout of such SCFs have revealed environmental benefits and attractive pay-back periods for large ferrochrome and integrated stainless steel producers looking to recover valuable components from discard slag, whilst enjoying a stream through which historical waste sources can be recycled. Transferring the molten discard slag, with its inherent store of sensible energy, directly to the SCF from the primary reduction process means molten feed-to-solid feed ratios can be optimized to yield a ferroalloy at reduced cost. Within the context of a steel producer, molten Electric Arc Furnace (EAF) and converter slags add further “sweeteners” high-in-metallic units that can be recovered to produce alloys of higher value. The processing of discard slag and historical wastes in a high throughput SCF supports sustainability and circularity. The resulting alloys are produced with a reduced carbon foot-print, the sensible heat of the molten slag can be recovered for heating or electrical generation, the barren slag is well suited to de-classification for use as an aggregate in concrete or as backfill in the construction sector, and a “dustbin” now exists on the site into which troublesome wastes can be fed, converted and neutralized without filling costly and environmentally detrimental landfills. This paper will introduce the process flow, technology and layout for SCFs designed to work within this context. The furnace metallurgy, M&E balance, furnace parameters and slag cleaning process essentials will be discussed. Options for additional value streams beyond the production of the ferroalloy will be explored, with the key environmental upside as the foundation of the process intent.
Migration and Spatial Misallocation in China

We structurally estimate the firm-level frictions across prefectures in China and quantify their aggregate and distributional implications. Based on a general equilibrium model with input and output distortions and migration, we show that the firm-level frictions are less dispersed and less correlated with productivity in richer prefectures. Counterfactual exercises show that reducing the within-prefecture misallocation increases the aggregate welfare, discourages migration towards large cities, and narrows the spatial inequality. Moreover, internal migration alleviates the impacts of micro-frictions on aggregate welfare and worsens their impacts on spatial inequality.
The Faster the Better? The Effect of Ultra-Fast Broadband on Students’ Performance

In this paper, we study the impact of ultra-fast broadband (UBB) access on student performance. These networks are based on optical fiber, allowing significantly higher speed compared to traditional copper-line connections. Our empirical analysis leverages on a unique dataset that combines information on broadband diffusion with data on student performance in 2nd, 5th, and 8th grade for the period 2012-2017. We exploit the staggered roll-out of UBB, starting from 2015. Through an event study approach, we find evidence of endogeneity between student performance and broadband diffusion. We deal with this issue through an instrumental variable approach that exploits plausibly exogenous variation in the diffusion of the essential UBB input. Our results suggest that ultra-fast connections significantly decrease students’ performance in Mathematics and Italian language in 8th grade. Instead, we do not find any significant effect in 2nd and 5th grade. Male students from low-educated parental backgrounds are those more adversely affected, especially if they attend schools with a low IT usage.
A re-examination of water in agate and its bearing on the agate genesis enigma

Abstract Dehydration of silanol and molecular water in 60 agates from 12 hosts with ages between 23 to 2717 Ma has been investigated using desiccators and high-temperature furnace heating. There are wide differences in the water data obtained under uncontrolled and fixed atmospheric water vapour pressure conditions. After agate acclimatization at 20°C and 46% relative humidity, the total water (silanol and molecular) was determined in powders and mini-cuboids by heating samples at 1200°C. Agates from hosts < 180 Ma all showed a greater mass loss using powders and demonstrate that after prolonged high-temperature heating, silanol water is partially-retained by the mini-cuboids. Desiccator dehydration of powders and slabs shows that powder preparation can produce water losses; this is particularly relevant in agates from hosts < 180 Ma. The identified problems have consequences for water quantification in agate and chalcedony using infrared or thermogravimetric techniques. Mobile and total water in agate is considered in relation to host-rock age, mogánite content and crystallite size. Links are observed between the various identified water contents allowing comment on quartz development and agate genesis. The water data also supports previous claims that agates from New Zealand and Brazil were formed long after their host.
Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language Models

A method for creating a vision-and-language (V&L) model is to extend a language model through structural modifications and V&L pre-training. Such an extension aims to make a V&L model inherit the capability of natural language understanding (NLU) from the original language model. To see how well this is achieved, we propose to evaluate V&L models using an NLU benchmark (GLUE). We compare five V&L models, including single-stream and dual-stream models, trained with the same pre-training. Dual-stream models, with their higher modality independence achieved by approximately doubling the number of parameters, are expected to preserve the NLU capability better. Our main finding is that the dual-stream scores are not much different than the single-stream scores, contrary to expectation. Further analysis shows that pre-training causes the performance drop in NLU tasks with few exceptions. These results suggest that adopting a single-stream structure and devising the pre-training could be an effective method for improving the maintenance of language knowledge in V&L extensions.
Treatment Effect Estimation with Noisy Conditioning Variables

When estimating causal effects, controlling for confounding factors is crucial, but these characteristics may not be observed. A widely adopted approach is to use proxy variables in place of the unobserved ideal controls. However, this approach generally suffers from measurement error bias. In this paper, I develop a new identification strategy that addresses this issue. I use proxy variables to construct a random variable conditional on which treatment variables become exogenous. The key idea is that, under appropriate conditions, there exists a one-to-one mapping between the distribution of unobserved confounding factors and the distribution of proxies. To satisfy overlap/support conditions, I use an additional variable, termed excluded variable, which satisfies certain exclusion restrictions and relevance conditions. I also establish asymptotic distributional results for flexible parametric and nonparametric estimators of the average structural function. I demonstrate empirical relevance of my results by estimating causal effects of Catholic schooling on college enrollment.
Arbitrarily Precise Quantum Alchemy

Doping compounds can be considered a perturbation to the nuclear charges in a molecular Hamiltonian. Expansions of this perturbation in a Taylor series, i.e. quantum alchemy, has been used in literature to assess millions of derivative compounds at once rather than enumerating them in costly quantum chemistry calculations. So far, it was unclear whether this series even converges for small molecules, whether it can be used for geometry relaxation and how strong this perturbation may be to still obtain convergent numbers. This work provides numerical evidence that this expansion converges and recovers the self-consistent energy of Hartree-Fock calculations. The convergence radius of this expansion is quantified for dimer examples and systematically evaluated for different basis sets, allowing for estimates of the chemical space that can be covered by perturbing one reference calculation alone. Besides electronic energy, convergence is shown for density matrix elements, molecular orbital energies, and density profiles, even for large changes in electronic structure, e.g. transforming He3 into H6. Subsequently, mixed alchemical and spatial derivatives are used to relax H2 from the electronic structure of He alone, highlighting a path to spatially relaxed quantum alchemy. Finally, the underlying code (APHF) which allows for arbitrary precision evaluation of restricted Hartree-Fock energies and arbitrary-order derivatives is made available to support future method development.
Nonradial neutrino emission upon black hole formation in core collapse supernovae

Black hole formation in a core-collapse supernova is expected to lead to a distinctive, abrupt drop in neutrino luminosity due to the engulfment of the main neutrino-producing regions as well as the strong gravitational redshift of those remaining neutrinos which do escape. Previous analyses of the shape of the cut-off have focused on specific trajectories or simplified models of bulk neutrino transport. In this article, we integrate over simple “ballistic” geodesics to investigate potential effects on the cut-off profile of including all neutrino emission angles from a collapsing surface in the Schwarzschild metric, and from a contracting equatorial mass ring in the Kerr metric. We find that the non-radial geodesics contribute to a softening of the cut-off in both cases. In addition, extreme rotation introduces significant changes to the shape of the tail which may be observable in future neutrino detectors, or combinations of detectors.
The Communication Value of a Quantum Channel

There are various ways to quantify the communication capabilities of a quantum channel. In this work we introduce the communication value (cv) of quantum channel, which describes the optimal probability of guessing the channel input from its output. By connecting to prior work on zero-error channel simulation, we show that the cv and its entanglement-assisted variant also offer dual interpretations as the classical communication cost for perfectly simulating different aspects of a channel using non-signaling resources. Our study involves characterizing the communication value as a generalized conditional min-entropy over the cone of separable operators. Using this characterization, we evaluate the cv for all qubit channels and higher-dimensional channels with certain symmetries. We find that the any entanglement-breaking channel has multiplicative cv when used in parallel with any other channel; the same is shown to hold for Pauli channels and partially depolarizing channels. In contrast, the cv is found to be non-multiplicative for a subset of the well-known Werner-Holevo channels. A final component of this work investigates relaxations of the channel cv to other cones such as the set of operators having a positive partial transpose (PPT).
Perturbative Euler-Heisenberg Lagrangian in a parity-violating Abelian gauge theory

In this paper, we examine perturbatively the behavior of the Euler-Heisenberg effective action in the presence of a novel axial coupling among the gauge field and the fermionic matter. This axial coupling is responsible to induce a parity-violating term in the extended form of the EulerHeisenberg effective action. In order to perform our analysis, we make use of a parametrization of the vector and axial coupling constants, gv and ga, in terms of a new coupling β. This parametrization allows us to explore a hidden symmetry in the effective Lagrangian under gv ↔ ga, which is verified for a class of diagrams, in particular with n = 4 photon external legs. This symmetry is explicitly observed in the loop calculations, where we determine the λi coefficients of LEH ext. = λ1F 2 + λ2G + λ3FG, in particular the coefficient λ3 related with the parity violation due to the axial coupling. As a phenomenological application of the results, we compute the relevant cross section for the light by light scattering through the extended Euler-Heisenberg effective action. ghasemkhani@ipm.ir v.rahmanpour@mail.sbu. ac.ir rodrigo.bufalo@ufla.br arsoto1@uc.cl
Recent Advances of Continual Learning in Computer Vision: An Overview

In contrast to batch learning where all training data is available at once, continual learning represents a family of methods that accumulate knowledge and learn continuously with data available in sequential order. Similar to the human learning process with the ability of learning, fusing, and accumulating new knowledge coming at different time steps, continual learning is considered to have high practical significance. Hence, continual learning has been studied in various artificial intelligence tasks. In this paper, we present a comprehensive review of the recent progress of continual learning in computer vision. In particular, the works are grouped by their representative techniques, including regularization, knowledge distillation, memory, generative replay, parameter isolation, and a combination of the above techniques. For each category of these techniques, both its characteristics and applications in computer vision are presented. At the end of this overview, several subareas, where continuous knowledge accumulation is potentially helpful while continual learning has not been well studied, are discussed.
Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery

We study the robust recovery of a low-rank matrix from sparsely and grossly corrupted Gaussian measurements, with no prior knowledge on the intrinsic rank. We consider the robust matrix factorization approach. We employ a robust `1 loss function and deal with the challenge of the unknown rank by using an overspecified factored representation of the matrix variable. We then solve the associated nonconvex nonsmooth problem using a subgradient method with diminishing stepsizes. We show that under a regularity condition on the sensing matrices and corruption, which we call restricted direction preserving property (RDPP), even with rank overspecified, the subgradient method converges to the exact low-rank solution at a sublinear rate. Moreover, our result is more general in the sense that it automatically speeds up to a linear rate once the factor rank matches the unknown rank. On the other hand, we show that the RDPP condition holds under generic settings, such as Gaussian measurements under independent or adversarial sparse corruptions, where the result could be of independent interest. Both the exact recovery and the convergence rate of the proposed subgradient method are numerically verified in the overspecified regime. Moreover, our experiment further shows that our particular design of diminishing stepsize effectively prevents overfitting for robust recovery under overparameterized models, such as robust matrix sensing and learning robust deep image prior. This regularization effect is worth further investigation.