Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network

This paper presents a meta-learning framework for few-shots One-Class Classiﬁcation (OCC) at test-time, a setting where labeled examples are only available for the positive class, and no supervision is given for the negative example. We consider that we have a set of ‘one-class classiﬁcation’ objective-tasks with only a small set of positive examples available for each task, and a set of training tasks with full supervision (i.e. highly imbalanced classiﬁcation). We propose an approach using order-equivariant networks to learn a ’meta’ binary-classiﬁer. The model will take as input an example to classify from a given task, as well as the corresponding supervised set of positive examples for this OCC task. Thus, the output of the model will be ’conditioned’ on the available positive example of a given task, allowing to predict on new tasks and new examples without labeled negative examples. In this paper, we are motivated by an astronomy application. Our goal is to identify if stars belong to a speciﬁc stellar group (the ’one-class’ for a given task), called stellar streams , where each stellar stream is a different OCC-task. We show that our method transfers well on unseen (test) synthetic streams, and outperforms the baselines even though it is not retrained and accesses a much smaller part of the data per task to predict (only positive supervision). We see however that it doesn’t transfer as well on the real stream GD-1. This could come from intrinsic differences from the synthetic and real stream, highlighting the need for consistency in the ’nature’ of the task for this method. However, light ﬁne-tuning improve performances and outperform our baselines. Our experiments show encouraging results to further explore meta-learning methods for OCC tasks.
Layer dynamics for the one dimensional $\varepsilon$-dependent Cahn-Hilliard / Allen-Cahn equation

We study the dynamics of the one-dimensional $\varepsilon$-dependent Cahn-Hilliard / Allen-Cahn equation within a neighborhood of an equilibrium of $N$ transition layers, that in general does not conserve mass. Two different settings are considered which differ in that, for the second, we impose a mass-conservation constraint in place of one of the zero-mass flux boundary conditions at $x=1$. Motivated by the study of Carr and Pego on the layered metastable patterns of Allen-Cahn in [10], and by this of Bates and Xun in [5] for the Cahn-Hilliard equation, we implement an $N$-dimensional, and a mass-conservative $ N-1$-dimensional manifold respectively; therein, a metastable state with $N$ transition layers is approximated. We then determine, for both cases, the essential dynamics of the layers (ode systems with the equations of motion), expressed in terms of local coordinates relative to the manifold used. In particular, we estimate the spectrum of the linearized Cahn-Hilliard / Allen-Cahn operator, and specify wide families of $\varepsilon$-dependent weights $\delta(\varepsilon)$, $\mu(\varepsilon)$, acting at each part of the operator, for which the dynamics are stable and rest exponentially small in $\varepsilon$. Our analysis enlightens the role of mass conservation in the classification of the general mixed problem into two main categories where the solution has a profile close to Allen-Cahn, or, when the mass is conserved, close to the Cahn-Hilliard solution.
Deep Q-trading

Algorithmic trading is a hot topic in machine learning. Compared to other methods, reinforcement learning (RL), particularly Q-learning, can learn decision rules directly with reasonable reward, and therefore is suitable for learning trading strategies. Recently, Q-learning based on deep neural models, also known as deep Q-learning, has been successfully applied to some challenging tasks like game playing and robot motion. In this paper, we propose to employ deep Q-learning to build an end-to-end deep Q-trading system which can automatically determine what position to hold at each trading time. Our experimental results show that the deep Q-trading system can outperform the buy-and-hold strategy as well as the strategy learned by recurrent reinforcement learning (RRL) that was known to be more eﬀective than Q-learning.
Estimation of the ozone formation rate in the atmospheric boundary layer over a background region of Western Siberia

The ozone formation rate in the atmospheric boundary layer (ABL) and the ozone inflow from the free atmosphere have been studied experimentally. The obtained estimates are based on the data of airborne sounding carried out over a background region of Western Siberia. As a result, it is obtained that the rate of ozone inflow from the upper atmospheric layers is only 20% of the rate of photochemical formation of ozone inside ABL. The vertical profiles of ozone flows in ABL have been additionally calculated based on the k-theory with the approach proposed by Troen and Mahrt. It has been shown in the calculations that the maximum of the ozone concentration in ABL is formed due to photochemical reactions from precursor gases.
Use of traditional medicine in treatment of migraine during coronavirus disease 2019 (COVID-19) pandemic- an online survey

Since the declaration of coronavirus disease 2019 (COVID-19) pandemic, patients with migraine were specifically vulnerable to worsening of their headaches. Traditional medicine (TM) has been used to treat headache disorders for centuries, especially during times of a healthcare emergencies, similar to the current pandemic. We aimed to assess the use of TM in treatment of migraine amid COVID-19 pandemic in Kuwait, using an online, self-administered questionnaire. A total of 1018 patients completed the survey. TM was used by 39.9% respondents. The greatest users of TM were those with older age (p =0.04), and longer disease duration (p =0.005). TM users were found to be more compliant to treatments than non-TM users (p<0.02). However, they reported significantly less communication with their physicians (p <0.001) during the pandemic. This study showed higher rates of TM use, as a way of self-treatment, among patients with migraine during COVID-19 pandemic. Neurologists should prepare their patients with rescue strategies for headache management, and new means of communication, to face these new normal challenges.
Variants of Concern Are Overrepresented Among Postvaccination Breakthrough Infections of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Washington State

Across 20 vaccine breakthrough cases detected at our institution, all 20 (100%) infections were due to variants of concern (VOC) and had a median Ct of 20.2 (IQR=17.1-23.3). When compared to 5174 contemporaneous samples sequenced in our laboratory, VOC were significantly enriched among breakthrough infections (p < .05).
A Risk Management Perspective on Macroprudential Policy

Macroprudential policymakers assess medium-term downside risks to the real economy arising from financial imbalances and implement policies aimed at managing those risks. In doing so, they face an inherent intertemporal trade-off between the expected growth and downside risks. This paper reviews the literature on Growth-at-Risk, embeds it in the wider literature on macroprudential policy, and proposes an empirical risk management framework that combines insights from the two literatures, by forecasting the entire real GDP growth distribution with a structural quantile vector autoregressive model. It accounts for direct and indirect interactions between financial vulnerabilities, financial stress and real GDP growth and allows for potential non-linear amplification effects. The framework provides policymakers with a macro-financial stress test to monitor downside risks to the economy and a macroprudential stance metric to quantify when interventions may be beneficial.
Institutional Investors, Households, and the Time-Variation in Expected Stock Returns

Abstract I document a new stylized fact: The higher the degree of institutional ownership (IO) in a portfolio, the more time-varying expected returns rather than changes in expected cash flows drive changes in its valuation. Empirical evidence suggests that institutions’ time-varying sensitivity to the risk of holding stocks translates into time-varying expected returns on high-IO stocks. In my model, imperfect risk sharing between different types of investors generates cross-sectional differences in return predictability based on ownership, even among a priori identical stocks. My findings suggest an economic rationale for weak return predictability of small stocks and predictability reversals of stocks and real estate investment trusts.
Firm Characteristics and Potential Output: A Growth Accounting Approach

We apply a growth accounting approach to estimate the contribution to potential output growth in Italy by firms with different characteristics. We do so by exploiting time series obtained by aggregating individual firm data. Results show that during the double-dip recession smaller firms provided the strongest negative contribution to potential output growth, while the recovery was driven by big ones. Young firms always give a positive contribution. Growth within sectors is the main driver of the dynamic of both aggregate trend total factor productivity and the capital labor ratio. Looking at sectoral composition effects, between 2014 and 2018 sectors with lower capital deepening have increased their share in the economy, holding back the aggregate figures.
Local and Global Agglomeration Patterns in the Banking Sector: The Calm in the Mid of a Storm

We compare the spatial agglomeration of banks’ branches in Italy across local areas (as identified by local labor market areas) to that of other services. Banks branches appear to be only weakly spatially agglomerated, their spatial distribution being similar to that of other services and to that of the firms from whom the demand for banking services is most likely to stem from. These findings have been stable throughout the period 1991-2015, despite the dramatic changes occurring in that time span (liberalization, ICT and the great recession). On the other hand, local areas with a higher (lower) presence of banking branches tend to be geographically clustered, displaying also a moderately decreasing pattern in this polarization. IC technologies partially contributed to this trend.
Moving Towards Behavioral Stadium Attendance Demand Research: First Lessons Learned from Exploring Football Spectator No-Show Behavior in Europe

Stadium attendance demand research is firmly anchored in the literature on the economics of sports. In fact, we sports economists, in particular, have tested both Rottenberg’s (1956) original stadium attendance demand specification and its extensions extensively over the last few decades. While there exists a great interest in gradually expanding the boundaries of this important research stream by subsequently analyzing additional sports in previously unexplored markets, our field's response to the potential methodological challenges arising from the quasi-standard use of aggregated attendance data seems to be less dedicated. To address these potential methodological challenges, we, i.e., my co-authors and I, have slowly begun moving towards behavioral stadium attendance research by analyzing a distinct spectator decision in the past few years: football spectator no-show behavior. Summarizing our recent empirical research from exploring such behavior in Europe, in this book chapter, we reflect on what we have learned over the past few years and, perhaps more importantly, what we think should be addressed in future stadium attendance demand research.
Inter-Firm Relationships and the Special Role of Common Banks

Using a novel dataset that combines information on customer-supplier trade relationships with information on firm-bank lending relationships, we show that common banks that lend to firms at both ends of a trade link strengthen such trade relationships. We use bank mergers that generate exogenous variations in presence of common banks to establish causality and show that common bank relationships between customers and suppliers increase trade relationships by 41.5%. The role of common bank is greater for more opaque supply chains and when the common bank is more informed. We argue that common banks bridge information gaps between trading partners and mitigate hold-up problems. Consistent with this hypothesis, we show that firms with a higher share of trading partners with whom they also share common banks have more concentrated customer base and invest more in relationship specific assets. Lastly, we show that common banks played a central role in facilitating provision of trade credit by suppliers during the Great Recession. Overall, our findings show the unique role of banks in driving inter-firm growth.
Institutional Constituents of Human Well-being in Papua New Guinea: A Second District-level Analysis

In the first discussion paper in this two paper series, Filer and Wood (2021) derived two measures of human well-being from the 2000 national census and sought to establish the relative strength of their association with 12 geographical variables in a comparative study of the 85 partially rural districts of Papua New Guinea. In this paper, we seek to compare the relative significance of these geographical variables and a smaller range of institutional variables for which we have been able to secure reasonable robust measures at a district level. We find that the degree of linguistic diversity or ‘ethno-linguistic fragmentation’ is quite strongly associated with a high level of child mortality, but is not associated with low levels of school attendance. Other institutional variables are only weakly associated with one or other of these two dependent variables, once the more significant of the geographical variables are taken into account. We conclude with a discussion of several issues that need to be taken into account in an interpretation of these findings. <br><br>This is the second discussion paper in a two paper series involving district-level analysis of human well-being in Papua New Guinea. See also: Filer, C &amp; Wood, T (2021). Geographical Constituents of Human Well-being in Papua New Guinea: A District-level Analysis. Development Policy Centre Discussion Paper No. 92, Crawford School of Public Policy, The Australian National University, Canberra, available at https://papers.ssrn. com/sol3/papers.cfm? abstract_id=3791993. <br><br>A codebook and spreadsheet containing measures for all the variables assessed in both discussion papers is available at https://doi.org/10.6084/m9.figshare. 14456229.
Strategic Similarity in Mergers and Acquisitions

Using textual analysis and the firm life-cycle theory to proxy for a company's competitive strategy, this paper empirically examines the strategic similarity hypothesis. The findings show that mergers and acquisitions deals are more likely between companies implementing the same strategy. Moreover, same strategy deals yield higher announcement returns, asset and sales growth. The effect is more pronounced in a highly competitive environment and within an industry, confirming that strategic misalignment acts as a constraint to the merged company's optimal response to investment opportunities and market threats. Overall, the results reveal that synergies obtained from the overlapping strategies constitute an important determinant of public mergers and acquisitions.
Energy-Aware and Delay-Sensitive Management of a Drone Delivery System

Problem description: This paper considers a drone delivery system that delivers packages to multiple locations, using a model that captures three distinctive features of interest: first, a battery-operated drone that can fly a limited number of distance units on a full battery; second, a set of demand locations with different flight distances and service-level requirements; and third, an adjustable flight speed to trade off energy efficiency for faster deliveries. By leveraging drone speed, job sequencing, and admission control, the system operator strives to achieve two managerial objectives: (i) to minimize energy- and congestion-related costs, and (ii) to maximize the use of the battery energy between consecutive battery replacements. Academic/practical relevance: E-commerce and shipping companies worldwide are testing and launching commercial drone delivery services to residences. According to a recent study, such services are expected to grow to $100 billion market by the end of the next decade. Our work focuses on improving drone delivery operations, which can reduce costs and improve user satisfaction. Methodology: Focusing on the first objective, we approximate system dynamics using diffusion processes and derive an optimal control policy for the approximating system. Results: Interpreting that policy in the context of the physical system while taking into account the second objective, we devise an implementable set of speed control, job sequencing, and admission strategies. The sequencing strategy, which selects a portfolio of jobs (termed as an activity) to be accomplished within a full battery cycle, results in a win-win scenario --- it allows both objectives to be approximately optimized at the same time. Managerial implications: Using the activity-based prioritization scheme provides opportunities to improve the overall performance of the system. Additionally, to achieve the intended job prioritization, the system operator can rely on a small set of activities that are of equal size to the number of job classes.
(Disruptive) innovations and rival firm outcomes

We show empirically that disruptive innovations significantly reduce rival firm growth and value in concentrated industries, consistent with the creative destruction hypothesis. Because these innovations make existing technologies obsolete, corporate investments and the amount of external financing to fund these investments also decrease. Disruptive innovations curb market power, thereby CEO compensation and CEO power also decline in affected firms. Overall, we show that disruptive innovation is a fierce force that intensifies competition, disciplines managers of rival firms, and strengthens business dynamism in the USA.
Co-opetition and Corporate R&D Disclosure: Evidence from Standard-Setting Organizations

We examine whether firms’ co-opetition (i.e., simultaneously competitive and cooperative) behaviors in standard-setting organizations (SSOs) affect their public R&D disclosure. We find that relative to matched control firms, firms that participate in SSOs increase their narrative R&D disclosure to the public after SSO participation. The effect of SSO participation on narrative R&D disclosure is more pronounced for firms with a greater ex-ante proprietary cost of disclosure and firms with higher investor information demand. Further analyses show that narrative R&D disclosure increases with the percentage of competitors in the SSO and that SSO participation raises not only the quantity but also the information content of such disclosure. Overall, the findings suggest that co-opetition inside SSOs facilitates the sharing of proprietary R&D information with competitors. This reduces firms’ proprietary cost of disclosure, incentivizing them to disclose more R&D information to the public.
Learning Generative Prior with Latent Space Sparsity Constraints

We address the problem of compressed sensing using a deep generative prior model and consider both linear and learned nonlinear sensing mechanisms, where the nonlinear one involves either a fully connected neural network or a convolutional neural network. Recently, it has been argued that the distribution of natural images do not lie in a single manifold but rather lie in a union of several submanifolds. We propose a sparsity-driven latent space sampling (SDLSS) framework and develop a proximal meta-learning (PML) algorithm to enforce sparsity in the latent space. SDLSS allows the range-space of the generator to be considered as a union-of-submanifolds. We also derive the sample complexity bounds within the SDLSS framework for the linear measurement model. The results demonstrate that for a higher degree of compression, the SDLSS method is more efficient than the state-of-the-art method. We first consider a comparison between linear and nonlinear sensing mechanisms on Fashion-MNIST dataset and show that the learned nonlinear version is superior to the linear one. Subsequent comparisons with the deep compressive sensing (DCS) framework proposed in the literature are reported. We also consider the effect of the dimension of the latent space and the sparsity factor in validating the SDLSS framework. Performance quantification is carried out by employing three objective metrics: peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), and reconstruction error (RE).
SpheCow: Flexible dynamical models for galaxies and dark matter haloes

Simple but flexible dynamical models are useful for many purposes, including serving as the starting point for more complex models or numerical simulations of galaxies, clusters, or dark matter haloes. We present SpheCow, a new light-weight and flexible code that allows one to easily explore the structure and dynamics of any spherical model. Assuming an isotropic or Osipkov-Merritt anisotropic orbital structure, the code can automatically calculate the dynamical properties of any model with either an analytical density profile or an analytical surface density profile as starting point. We have extensively validated SpheCow using a combination of comparisons to analytical and high-precision numerical calculations, as well as the calculation of inverse formulae. SpheCow contains readily usable implementations for many standard models, including the Plummer, Hernquist, NFW, Einasto, Sérsic, and Nuker models. The code is publicly available as a set of C++ routines and as a Python module, and it is designed to be easily extendable, in the sense that new models can be added in a straightforward way. We demonstrate this by adding two new families of models in which either the density slope or the surface density slope is described by an algebraic sigmoid function. We advocate the use of the SpheCow code to investigate the full dynamical structure for models for which the distribution function cannot be expressed analytically and to explore a much wider range of models than is possible using analytical models alone.
Real-time Monocular Depth Estimation with Sparse Supervision on Mobile

Monocular (relative or metric) depth estimation is a critical task for various applications, such as autonomous vehicles, augmented reality and image editing. In recent years, with the increasing availability of mobile devices, accurate and mobile-friendly depth models have gained importance. Increasingly accurate models typically require more computational resources, which inhibits the use of such models on mobile devices. The mobile use case is arguably the most unrestricted one, which requires highly accurate yet mobile-friendly architectures. Therefore, we try to answer the following question: How can we improve a model without adding further complexity (i.e. parameters)? Towards this end, we systematically explore the design space of a relative depth estimation model from various dimensions and we show, with key design choices and ablation studies, even an existing architecture can reach highly competitive performance to the state of the art, with a fraction of the complexity. Our study spans an in-depth backbone model selection process, knowledge distillation, intermediate predictions, model pruning and loss rebalancing. We show that our model, using only DIW as the supervisory dataset, achieves 0.1156 WHDR on DIW with 2.6M parameters and reaches 37 FPS on a mobile GPU, without pruning or hardware-specific optimization. A pruned version of our model achieves 0.1208 WHDR on DIW with 1M parameters and reaches 44 FPS on a mobile GPU.