Miracle-dz/UniFAR-FTUs huggingface dataset:
Spike in Asthma Healthcare Presentations in Eastern England during June 2021: A Retrospective Observational Study Using Syndromic Surveillance Data

Background Thunderstorm asthma is often characterised by a sudden surge in patients presenting with exacerbated symptoms of asthma linked to thunderstorm activity. On 17 June 2021, Public Health England (PHE) observed a large spike in health care seeking behaviour by patients presenting with asthma and difficulty breathing symptoms across parts of England. Objectives To describe the epidemiology of the observed asthma spike and explore available meteorological and environmental data to understand potential causes of this episode. Design A retrospective observational study was conducted of patient visits to health care services in England as monitored through surveillance systems routinely operated by PHE. The number of presentations during the asthma event was compared to expected levels for the overall population and across specific regions. Setting Healthcare services in England. Main outcome measures Number of patients presenting to healthcare services for asthma- and difficulty breathing-type symptoms. Results Spikes in asthma and difficulty breathing were detected across several PHE syndromic surveillance systems. Across affected areas ED attendances for asthma increased by 560% on 17 June compared to the average number of weekday daily attendances during the previous 4 weeks. GP out of hours contacts increased by 422%, NHS 111 calls 193%, NHS 111 online assessments 581% and ambulance call outs 54%. Increases were particularly noted in patient age groups 5-14 and 15-44 years. In non-affected regions, increases were small (<10%) or decreased, except for NHS 111 online assessments where there was an increase of 39%. A review of the meteorological conditions showed several localised, weak or moderate thunderstorms specifically across parts of South East England on the night of June 16. Conclusions An unprecedented episode of asthma was recorded in England, characterised by significant surges in health care seeking behaviour. However, the links to meteorologically defined thunderstorm activity were not as clear as previous episodes, with less evidence of severe thunderstorm activity in those areas affected, prompting further discussion about the causes of these events and implications for public health management of the risk.
The Volatility of Economic Policy Uncertainty

Theory as well as empirics suggest that both the level and the volatility of uncertainty impact important economic variables. There is a need to extend models of uncertainty to the volatility of uncertainty. We analyse the dynamics of the Economic Policy Uncertainty index developed by (Baker et al., 2016) and show that for four major economies in Europe – France, Germany, Italy and the UK – between 1997 and 2019, consider-able portions of both the level and the volatility of economic policy uncertainty were generated by spillovers. Spillovers in the volatility of economic policy uncertainty was higher than spillovers in levels during major crises. These findings are relevant to the appraisal of economic policy uncertainty episodes in major trading partners.
What Drives Global Value Chain Participation?

Cross-border production sharing has intensified in recent decades, leading to the formation and spread of global value chains (GVC). Using a dataset containing more than 150 countries over 1990-2018, our paper tries to identify what drives backward GVC participation and forward GVC participation at the country and aggregate trade levels. We complement this general exercise with a gravity model analysis of the determinants of bilateral foreign value-added in exports. The econometric analyses highlight structural factors such as aggregate income, level of industrialization and distance to economic hubs as highly significant for GVC trade. Foreign direct investment inflows strongly influence GVC participation and are stronger for backward participation. Trade agreements and their depth boost GVC participation, although the expansionary effect of deeper trade agreements on GVC trade decreases over time. We replicate our general analysis for developed and developing countries to account for the differential effects of GVC drivers at different levels of development. We ensure that our gravity estimates are theoretically and analytically consistent by using the Poisson Pseudo Maximum Likelihood estimator and incorporating multilateral resistance.
The Rise of Guard Labor in the United States: Evidence from Local Labor Markets

We examine the rise of “Guard Labor”—defined as occupations devoted to labor extraction—at the commuting zone level in the United States. Between 1950 and 2017 the share of the labor force employed in guard labor increased from 19.5 percent to 29.4 percent. We explore the local labor market correlates of guard labor and validate several predictions of the labor discipline model, while extending the model to the context of contemporary racial capitalism. The share of the labor force employed in guard labor is positively correlated with the household income Gini coefficient, negatively correlated with the fraction of the labor force in a union, and positively correlated with contemporary measures of local racial animosity. Consistent with the labor discipline model, an increase in the share of the labor force employed in supervisory occupations has a significant negative effect on nonsupervisory wages. JEL Classification: O17, P16, Z13, B51, B52
Market Feedback and Access to Buy-side Research: Evidence From Monthly Mutual Fund Portfolio Disclosures

Using shifts in mutual funds’ holdings disclosure frequency from quarterly to monthly to proxy for increased access to buy-side research, we find evidence indicating that greater access to buy-side research reduces the sensitivity of corporate investment to stock prices. Consistent with a crowding-out mechanism, the evidence suggests that monthly portfolio disclosures discourage information production activities by investors and, consequently, reduce corporate managers’ ability to learn from prices. This effect increases with managers’ incentives to learn from prices and investors’ potential use of monthly fund disclosures. Our findings highlight the potential cost of more frequent portfolio disclosures for the real economy.
End-of-the-year economic growth and time-varying expected returns: a reexamination

Møller and Rangvid (2015) report that economic growth at the end of the year is a strong predictor of future stock returns for the post-WWII period, whereas economic growth during the rest of the year does not. Revisiting these results with an extended period 1926-2020, we find that this pattern does not hold. The predictive ability of end-of-the-year economic growth is generally fragile. Out-of-sample analysis shows that the fourth quarter economic growth outperforms the historical average benchmark mainly in the 1970s and the recent financial crisis even for the post-WWII sample.
Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits

In many public health settings, there is a perceived tension between allocating resources to known vulnerable areas and learning about the overall prevalence of the problem. Inspired by a door-to-door Covid-19 testing program we helped design, we combine multi-armed bandit strategies and insights from sampling theory to demonstrate how to recover accurate prevalence estimates while continuing to allocate resources to at-risk areas. We use the outbreak of an infectious disease as our running example. The public health setting has several characteristics distinguishing it from typical bandit settings, such as distribution shift (the true disease prevalence is changing with time) and batched sampling (multiple decisions must be made simultaneously). Nevertheless, we demonstrate that several bandit algorithms are capable out-performing greedy resource allocation strategies, which often perform worse than random allocation as they fail to notice outbreaks in new areas.
Scale-Free Adversarial Multi-Armed Bandit with Arbitrary Feedback Delays

We consider the Scale-Free Adversarial Multi-Armed Bandit (MAB) problem with unrestricted feedback delays. In contrast to the standard assumption that all losses are $[0,1]$-bounded, in our setting, losses can fall in a general bounded interval $[-L, L]$, unknown to the agent beforehand. Furthermore, the feedback of each arm pull can experience arbitrary delays. We propose a novel approach named Scale-Free Delayed INF (SFD-INF) for this novel setting, which combines a recent"convex combination trick"together with a novel doubling and skipping technique. We then present two instances of SFD-INF, each with carefully designed delay-adapted learning scales. The first one SFD-TINF uses $\frac 12$-Tsallis entropy regularizer and can achieve $\widetilde{\mathcal O}(\sqrt{K(D+T)}L)$ regret when the losses are non-negative, where $K$ is the number of actions, $T$ is the number of steps, and $D$ is the total feedback delay. This bound nearly matches the $\Omega((\sqrt{KT}+\sqrt{D\log K})L)$ lower-bound when regarding $K$ as a constant independent of $T$. The second one, SFD-LBINF, works for general scale-free losses and achieves a small-loss style adaptive regret bound $\widetilde{\mathcal O}(\sqrt{K\mathbb{E}[\tilde{\mathfrak L}_T^2]}+\sqrt{KDL})$, which falls to the $\widetilde{\mathcal O}(\sqrt{K(D+T)}L)$ regret in the worst case and is thus more general than SFD-TINF despite a more complicated analysis and several extra logarithmic dependencies. Moreover, both instances also outperform the existing algorithms for non-delayed (i.e., $D=0$) scale-free adversarial MAB problems, which can be of independent interest.
Vaccine skepticism detection by network embedding

We demonstrate the applicability of network embedding to vaccine skepticism, a controversial topic of long-past history. With the Covid-19 pandemic outbreak at the end of 2019, the topic is more important than ever. Only a year after the first international cases were registered, multiple vaccines were developed and passed clinical testing. Besides the challenges of development, testing, and logistics, another factor that might play a significant role in the fight against the pandemic are people who are hesitant to get vaccinated, or even state that they will refuse any vaccine offered to them. Two groups of people commonly referred to as a) pro-vaxxer, those who support vaccinating people b) vax-skeptic, those who question vaccine efficacy or the need for general vaccination against Covid-19. It is very difficult to tell exactly how many people share each of these views. It is even more difficult to understand all the reasoning why vax-skeptic opinions are getting more popular. In this work, our intention was to develop techniques that are able to efficiently differentiate between pro-vaxxer and vax-skeptic content. After multiple data preprocessing steps, we analyzed the tweet text as well as the structure of user interactions on Twitter. We deployed several node embedding and community detection models that scale well for graphs with millions of edges.
How Should AI Interpret Rules? A Defense of Minimally Defeasible Interpretive Argumentation

Can artificially intelligent systems follow rules? The answer might seem an obvious ‘yes’, in the sense that all (current) AI strictly acts in accordance with programming code constructed from highly formalized and well-defined rulesets. But here I refer to the kinds of rules expressed in human language that are the basis of laws, regulations, codes of conduct, ethical guidelines, and so on. The ability to follow such rules, and to reason about them, is not nearly as clear-cut as it seems on first analysis. Real-world rules are unavoidably rife with open-textured terms, which imbue rules with a possibly infinite set of possible interpretations. Narrowing down this set requires a complex reasoning process that is not yet within the scope of contemporary AI. This poses a serious problem for autonomous AI: If one cannot reason about open-textured terms, then one cannot reason about (or in accordance with) real-world rules. And if one cannot reason about real-world rules, then one cannot: follow human laws, comply with regulations, act in accordance with written agreements, or even obey mission-specific commands that are anything more than trivial. But before tackling these problems, we must first answer a more fundamental question: Given an open-textured rule, what is its correct interpretation? Or more precisely: How should our artificially intelligent systems determine which interpretation to consider correct? In this essay, I defend the following answer: Rule-following AI should act in accordance with the interpretation best supported by minimally defeasible interpretive arguments (MDIA). In Section 2, I will introduce and defend the need for AI which can produce interpretations of rules that are supported by interpretive argumentation, and advocate for AI that is argument-justified rather than merely explainable. But in order to recognize interpretive argument quality, we need a way to estimate argument strength. Section 3 argues that the minimal defeasibility standard is our best hope. Finally, I address anticipated counterarguments and possible points of confusion in Section 4.
CTRN: Class-Temporal Relational Network for Action Detection

Action detection is an essential and challenging task, especially for densely labelled datasets of untrimmed videos. There are many real-world challenges in those datasets, such as composite action, co-occurring action, and high temporal variation of instance duration. For handling these challenges, we propose to explore both the class and temporal relations of detected actions. In this work, we introduce an end-to-end network: Class-Temporal Relational Network (CTRN). It contains three key components: (1) The Representation Transform Module filters the class-specific features from the mixed representations to build graph-structured data. (2) The Class-Temporal Module models the class and temporal relations in a sequential manner. (3) G-classifier leverages the privileged knowledge of the snippet-wise co-occurring action pairs to further improve the co-occurring action detection. We evaluate CTRN on three challenging densely labelled datasets and achieve state-of-the-art performance, reflecting the effectiveness and robustness of our method.
Beyond Lp clipping: Equalization-based Psychoacoustic Attacks against ASRs

Automatic Speech Recognition (ASR) systems convert speech into text and can be placed into two broad categories: traditional and fully end-to-end. Both types have been shown to be vulnerable to adversarial audio examples that sound benign to the human ear but force the ASR to produce malicious transcriptions. Of these attacks, only the"psychoacoustic"attacks can create examples with relatively imperceptible perturbations, as they leverage the knowledge of the human auditory system. Unfortunately, existing psychoacoustic attacks can only be applied against traditional models, and are obsolete against the newer, fully end-to-end ASRs. In this paper, we propose an equalization-based psychoacoustic attack that can exploit both traditional and fully end-to-end ASRs. We successfully demonstrate our attack against real-world ASRs that include DeepSpeech and Wav2Letter. Moreover, we employ a user study to verify that our method creates low audible distortion. Specifically, 80 of the 100 participants voted in favor of all our attack audio samples as less noisier than the existing state-of-the-art attack. Through this, we demonstrate both types of existing ASR pipelines can be exploited with minimum degradation to attack audio quality.
Plug-and-Play Few-shot Object Detection with Meta Strategy and Explicit Localization Inference

Aiming at recognizing and localizing the object of novel categories by a few reference samples, few-shot object detection is a quite challenging task. Previous works often depend on the fine-tuning process to transfer their model to the novel category and rarely consider the defect of fine-tuning, resulting in many drawbacks. For example, these methods are far from satisfying in the low-shot or episode-based scenarios since the fine-tuning process in object detection requires much time and high-shot support data. To this end, this paper proposes a plug-and-play few-shot object detection (PnP-FSOD) framework that can accurately and directly detect the objects of novel categories without the fine-tuning process. To accomplish the objective, the PnP-FSOD framework contains two parallel techniques to address the core challenges in the few-shot learning, i.e., across-category task and few-annotation support. Concretely, we first propose two simple but effective meta strategies for the box classifier and RPN module to enable the across-category object detection without fine-tuning. Then, we introduce two explicit inferences into the localization process to reduce its dependence on the annotated data, including explicit localization score and semi-explicit box regression. In addition to the PnP-FSOD framework, we propose a novel one-step tuning method that can avoid the defects in fine-tuning. It is noteworthy that the proposed techniques and tuning method are based on the general object detector without other prior methods, so they are easily compatible with the existing FSOD methods. Extensive experiments show that the PnP-FSOD framework has achieved the state-of-the-art few-shot object detection performance without any tuning method. After applying the one-step tuning method, it further shows a significant lead in both efficiency, precision, and recall, under varied few-shot evaluation protocols.
2D Grid Map Generation for Deep-Learning-based Navigation Approaches

In the last decade, autonomous navigation for robotics has been leveraged by deep learning and other approaches based on machine learning. These approaches have demonstrated significant advantages in robotics performance. But they have the disadvantage that they require a lot of data to infer knowledge. In this paper, we present an algorithm for building 2D maps with attributes that make them useful for training and testing machine-learning-based approaches. The maps are based on dungeons environments where several random rooms are built and then those rooms are connected. In addition, we provide a dataset with 10,000 maps produced by the proposed algorithm and a description with extensive information for algorithm evaluation. Such information includes validation of path existence, the best path, distances, among other attributes. We believe that these maps and their related information can be useful for robotics enthusiasts and researchers who want to test deep learning approaches. The dataset is available at https://github.com/gbriel21/map2D dataSet.git.
Variational framework for partially-measured physical system control: examples of vision neuroscience and optical random media

To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured. The complexity of full measurements of the system scales with its size. When exposed to real-world conditions, such as perturbations or time-varying settings, the system calibrated for a fixed working condition might require non-trivial re-calibration, a process that could be prohibitively expensive, inefficient and impractical for real-world use cases. In this work, we propose a learning procedure to obtain a desired target output from a physical system. We use Variational Auto-Encoders (VAE) to provide a generative model of the system function and use this model to obtain the required input of the system that produces the target output. We showcase the applicability of our method for two datasets in optical physics and neuroscience.
Observational Constraints on the Maximum Energies of Accelerated Particles in Supernova Remnants: Low Maximum Energies and a Large Variety

Supernova remnants (SNRs) are thought to be the most promising sources of Galactic cosmic rays. One of the principal questions is whether they are accelerating particles up to the maximum energy of Galactic cosmic rays (∼PeV). In this work, a systematic study of gamma-ray-emitting SNRs is conducted as an advanced study of Suzuki et al. Our purpose is to newly measure the evolution of maximum particle energies with increased statistics and better age estimates. We model their gamma-ray spectra to constrain the particle-acceleration parameters. Two candidates of the maximum energy of freshly accelerated particles, the gamma-ray cutoff and break energies, are found to be well below PeV. We also test a spectral model that includes both the freshly accelerated and escaping particles to estimate the maximum energies more reliably, but no tighter constraints are obtained with current statistics. The average time dependences of the cutoff energy (∝t −0.81±0.24) and break energy (∝t −0.77±0.23) cannot be explained with the simplest acceleration condition (Bohm limit) and require shock–ISM (interstellar medium) interaction. The average maximum energy during lifetime is found to be ≲20 TeV (tM/1kyr)−0.8 with t M being the age at the maximum, which reaches PeV if t M ≲ 10 yr. The maximum energies during lifetime are suggested to have a variety of 1.1–1.8 dex from object to object. Although we cannot isolate the cause of this variety, this work provides an important clue to understanding the microphysics of particle acceleration in SNRs.
AUTO-DISCERN: Autonomous Driving Using Common Sense Reasoning

Driving an automobile involves the tasks of observing surroundings, then making a driving decision based on these observations (steer, brake, coast, etc.). In autonomous driving, all these tasks have to be automated. Autonomous driving technology thus far has relied primarily on machine learning techniques. We argue that appropriate technology should be used for the appropriate task. That is, while machine learning technology is good for observing and automatically understanding the surroundings of an automobile, driving decisions are better automated via commonsense reasoning rather than machine learning. In this paper, we discuss (i) how commonsense reasoning can be automated using answer set programming (ASP) and the goal-directed s(CASP) ASP system, and (ii) develop the AUTO-DISCERN system using this technology for automating decision-making in driving. The goal of our research, described in this paper, is to develop an autonomous driving system that works by simulating the mind of a human driver. Since driving decisions are based on human-style reasoning, they are explainable, their ethics can be ensured, and they will always be correct, provided the system modeling and system inputs are correct.