Miracle-dz/UniFAR-FTUs huggingface dataset:
Can Decentralisation Be a Force for Bad? New Evidence from Decentralising Environmental Clearances in India

We exploit India's 2006 reform of Environmental Impact Assessment (EIA) regulations to assess whether decentralising environmental regulations improve firms' pollution abatement behaviours and their impacts on firm performance. Using a nationally representative panel of registered manufacturing firms from 2002 to 2018, we exploit temporal and industry-level variation using a difference-in-differences strategy to estimate impacts of decentralisation at the firm, industry-state and district levels. We then explore heterogeneous effects of decentralisation by regulatory stringency, prior regulatory compliance, and construction of local regulatory authorities at the state level. For formal sector firms, we find that decentralisation reduced the likelihood of installing pollution control machineries, led to large and persistent reductions in investments and employment, but also reduced fuel intensity. We further examine spillover effects of decentralisation on unregistered firms in the informal sector, finding that districts more exposed to decentralisation saw faster informal sector employment and output growth. Overall, these results suggest India's decentralisation of environmental clearance was not effective at improving firm pollution control and brought with it economic costs.
Optimistic and Pessimistic Disagreement and the Cross Section of Stock Returns

We decompose total disagreement about macro variables into the disagreement among optimists (i.e., forecasters whose forecast exceeds a certain threshold) and pessimists. Optimistic (pessimistic) forecasters tend to disagree more in good (bad) times. Pessimistic (optimistic) disagreement commands a negative and significant (positive, although often (insignificant) risk premium and total disagreement is often insignificant when included in the same regression. These results are robust across a variety of empirical specifications and sets of test assets. A theoretical model, in which the risk premia of optimistic and pessimistic disagreement depend in a non-trivial way on forecasters’ beliefs and on the joint impact of optimistic and pessimistic disagreement on the price of the assets used to speculate on individual beliefs, rationalizes the empirical findings.
Frequency Dependent Risks in the Factor Zoo

I propose a novel framework to quantify frequency-dependent risks in the factor zoo. Empirically, the linear stochastic discount factor (SDF) comprised of the first few low-frequency principal components (PCs) yields an out-of-sample monthly Sharpe ratio of 0.37, and other smaller low-frequency PCs are redundant. In contrast, the SDFs consisting of high-frequency and canonical PCs are dense and fail to identify slow-moving conditional information in asset returns. Moreover, I decompose the low-frequency SDF into two orthogonal priced components. The first component, linear in high-frequency PCs, is almost serially uncorrelated and relates to discount-rate news, intermediary factors, jump risk, and investor sentiment. The second component exhibits a persistent conditional dynamic and captures business-cycle risks related to consumption and GDP growth. Overall, asset pricing theory has frequency-dependent relevance.
Cost asymmetry and firm value

In this paper we explore the association between asymmetric cost behavior and firm value. Using a large sample of U.S. data, we find a robust negative relationship between cost asymmetry and firm value. We then explore whether the resource adjustment, managerial expectations, and agency theories of asymmetric cost behaviour affect the negative relation, and find support for the managerial expectation and agency theories. Furthermore, we find evidence that the detrimental impact of asymmetric cost behaviour on firm value is mediated partially through the cost of equity and cash flow channels. Further investigation suggests that the detrimental impact of cost asymmetry on firm value is stronger in the presence of high information asymmetry. We enrich the cost management literature by integrating cost asymmetry with corporate finance.
Motives for economic migration: a review

The present paper sheds light on the motives of economic migrants―an aspect that has not been discussed at a general level so far. Previous work has only focused on specific fields of interest and used different terminologies, hence impeding comparison and the synthesis of findings across studies. Derived from theoretical, empirical, and analytical research outcomes, the paper concludes that economic migrants’ movements are influenced by the socio-demographic factors of ‘age’ and ‘education’ and are motivated by both the economic motives of ‘expected income’ and ‘employment’ and the economic-related motives of ‘corruption’, ‘amenities’ and ‘happiness’. Furthermore, these motives reveal a typical profile of economic migrants: a typical economic migrant is characterized by being of working age, highly educated and male, and by wishing to migrate to developed countries to achieve a fulfilling life.
Liquidation value of productive assets and product differentiation

This study examines the choice of individual companies to adapt productive assets (PAs) to specific production. To soften competition, companies may modify their assets to increase product differentiation. However, this decision alters the liquidation value of the assets in the case of bankruptcy for the presence of redeployment costs (larger for specialized assets) faced by potential buyers. We determine the equilibrium level of specialization of PAs, pointing to a novel trade-off between product market differentiation and the resale value of PAs. We find that industry entry and redeployment costs, together with the number of potential bidders in the second-hand market of PAs are important factors in explaining the degree of product differentiation.
Foreign bias in equity portfolios: Informational advantage or familiarity bias?

We use the European Central Bank's new and detailed database of European equity holdings by households to test two competing theories of international biases in equity portfolios, that they reflect either informational advantages or familiarity bias. The database allows look-through handling of investments via mutual funds etc, and reveals that home bias is smaller than usually believed. We find that both home bias and foreign bias are positively associated with the Cremers et al (2009) ActiveShare measure and negatively with the fraction of the country sub-portfolio invested via mutual funds, both of which are consistent with information effects rather than familiarity bias.
Part & Whole Extraction: Towards A Deep Understanding of Quantitative Facts for Percentages in Text

We study the problem of quantitative facts extraction for text with percentages. For example, given the sentence"30 percent of Americans like watching football, while 20% prefer to watch NBA. ", our goal is to obtain a deep understanding of the percentage numbers ("30 percent"and"20%") by extracting their quantitative facts: part ("like watching football"and"prefer to watch NBA") and whole ("Americans). These quantitative facts can empower new applications like automated infographic generation. We formulate part and whole extraction as a sequence tagging problem. Due to the large gap between part/whole and its corresponding percentage, we introduce skip mechanism in sequence modeling, and achieved improved performance on both our task and the CoNLL-2003 named entity recognition task. Experimental results demonstrate that learning to skip in sequence tagging is promising.
Reconstruction of Mutation Laws in Heterogeneous Tumours with Local and Nonlocal Dynamics

Cancer cell mutations occur when cells undergo multiple cell divisions, and these mutations can be spontaneous or environmentally-induced. The mechanisms that promote and sustain these mutations are still not fully understood. This study deals with the identification (or reconstruction) of the usually unknown cancer cell mutation law, which lead to the transformation of a primary tumour cell population into a secondary, more aggressive cell population. We focus on local and nonlocal mathematical models for cell dynamics and movement, and identify these mutation laws from macroscopic tumour snapshot data collected at some later stage in the tumour evolution. In a local cancer invasion model, we first reconstruct the mutation law when we assume that the mutations depend only on the surrounding cancer cells (i.e., the ECM plays no role in mutations). Second, we assume that the mutations depend on the ECM only, and we reconstruct the mutation law in this case. Third, we reconstruct the mutation when we assume that there is no prior knowledge about the mutations. Finally, for the nonlocal cancer invasion model, we reconstruct the mutation law that depends on the cancer cells and on the ECM. For these numerical reconstructions, our approximations are based on the finite difference method combined with the finite elements method. As the inverse problem is ill-posed, we use the Tikhonov regularisation technique in order to regularise the solution. Stability of the solution is examined by adding additive noise into the measurements.
Regime-Switching Density Forecasts Using Economists' Scenarios

We propose an approach for generating macroeconomic density forecasts that incorporate information on multiple scenarios defined by experts. We adopt a regime-switching framework in which sets of scenarios ("views") are used as Bayesian priors on economic regimes. Predictive densities coming from different views are then combined by optimizing objective functions of density forecasting. We illustrate the approach with an empirical application to quarterly real-time forecasts of U.S. GDP growth, in which we exploit the Fed's macroeconomic scenarios used for bank stress tests. We show that the approach achieves good accuracy in terms of average predictive scores and good calibration of forecast distributions. Moreover, it can be used to evaluate the contribution of economists' scenarios to density forecast performance.
Impact of the New 65As(p,γ)66Se Reaction Rate on the Two-proton Sequential Capture of 64Ge, Weak GeAs Cycles, and Type I X-Ray Bursts Such as the Clocked Burster GS 1826−24

We reassess the 65As(p,γ)66Se reaction rates based on a set of proton thresholds of 66Se, S p(66Se), estimated from the experimental mirror nuclear masses, theoretical mirror displacement energies, and full p f-model space shell-model calculation. The self-consistent relativistic Hartree–Bogoliubov theory is employed to obtain the mirror displacement energies with much reduced uncertainty, and thus reducing the proton-threshold uncertainty up to 161 keV compared to the AME2020 evaluation. Using the simulation instantiated by the one-dimensional multi-zone hydrodynamic code, Kepler, which closely reproduces the observed GS 1826−24 clocked bursts, the present forward and reverse 65As(p,γ)66Se reaction rates based on a selected S p(66Se) = 2.469 ± 0.054 MeV, and the latest 22Mg(α,p)25Al, 56Ni(p,γ)57Cu, 57Cu(p,γ)58Zn, 55Ni(p,γ)56Cu, and 64Ge(p,γ)65As reaction rates, we find that though the GeAs cycles are weakly established in the rapid-proton capture process path, the 65As(p,γ)66Se reaction still strongly characterizes the burst tail end due to the two-proton sequential capture on 64Ge, not found by the Cyburt et al. sensitivity study. The 65As(p,γ)66Se reaction influences the abundances of nuclei A = 64, 68, 72, 76, and 80 up to a factor of 1.4. The new S p(66Se) and the inclusion of the updated 22Mg(α,p)25Al reaction rate increases the production of 12C up to a factor of 4.5, which is not observable and could be the main fuel for a superburst. The enhancement of the 12C mass fraction alleviates the discrepancy in explaining the origin of the superburst. The waiting point status of and two-proton sequential capture on 64Ge, the weak-cycle feature of GeAs at a region heavier than 64Ge, and the impact of other possible S p(66Se) are also discussed.
Learning Explicit and Implicit Latent Common Spaces for Audio-Visual Cross-Modal Retrieval

Learning common subspace is prevalent way in cross-modal retrieval to solve the problem of data from different modalities having inconsistent distributions and representations that cannot be directly compared. Previous cross-modal retrieval methods focus on projecting the cross-modal data into a common space by learning the correlation between them to bridge the modality gap. However, the rich semantic information in the video and the heterogeneous nature of audio-visual data leads to more serious heterogeneous gaps intuitively, which may lead to the loss of key semantic content of video with single clue by the previous methods when eliminating the modality gap, while the semantics of the categories may undermine the properties of the original features. In this work, we aim to learn effective audio-visual representations to support audio-visual cross-modal retrieval (AVCMR). We propose a novel model that maps audio-visual modalities into two distinct shared latent subspaces: explicit and implicit shared spaces. In particular, the explicit shared space is used to optimize pairwise correlations, where learned representations across modalities capture the commonalities of audio-visual pairs and reduce the modality gap. The implicit shared space is used to preserve the distinctive features between modalities by maintaining the discrimination of audio/video patterns from different semantic categories. Finally, the fusion of the features learned from the two latent subspaces is used for the similarity computation of the AVCMR task. The comprehensive experimental results on two audio-visual datasets demonstrate that our proposed model for using two different latent subspaces for audio-visual cross-modal learning is effective and significantly outperforms the state-of-the-art cross-modal models that learn features from a single subspace.
Eigenvalue Bounds for Double Saddle-Point Systems

We derive bounds on the eigenvalues of a generic form of double saddle-point matrices. The bounds are expressed in terms of extremal eigenvalues and singular values of the associated block matrices. Inertia and algebraic multiplicity of eigenvalues are considered as well. The analysis includes bounds for preconditioned matrices based on block diagonal preconditioners using Schur complements, and it is shown that in this case the eigenvalues are clustered within a few intervals bounded away from zero. Analysis for approximations of Schur complements is included. Some numerical experiments validate our analytical findings.
Perspective-neutral approach to quantum frame covariance for general symmetry groups

In the absence of external relata, internal quantum reference frames (QRFs) appear widely in the literature on quantum gravity, gauge theories and quantum foundations. Here, we extend the perspective-neutral approach to QRF covariance to general unimodular Lie groups. This is a framework that links internal QRF perspectives via a manifestly gauge-invariant Hilbert space in the form of"quantum coordinate transformations", and we clarify how it is a quantum extension of special covariance. We model the QRF orientations as coherent states which give rise to a covariant POVM, furnishing a consistent probability interpretation and encompassing non-ideal QRFs whose orientations are not perfectly distinguishable. We generalize the construction of relational observables, establish a variety of their algebraic properties and equip them with a transparent conditional probability interpretation. We import the distinction between gauge transformations and physical symmetries from gauge theories and identify the latter as QRF reorientations. The"quantum coordinate maps"into an internal QRF perspective are constructed via a conditioning on the QRF's orientation, generalizing the Page-Wootters formalism and a symmetry reduction procedure. We find two types of QRF transformations: gauge induced"quantum coordinate transformations"as passive unitary changes of description and symmetry induced active changes of relational observables from one QRF to another. We reveal new effects: (i) QRFs with non-trivial orientation isotropy groups can only resolve isotropy-group-invariant properties of other subsystems; (ii) in the absence of symmetries, the internal perspective Hilbert space"rotates"through the kinematical subsystem Hilbert space as the QRF changes orientation. Finally, we invoke the symmetries to generalize the quantum relativity of subsystems before comparing with other approaches. [Abridged]
Enhancing Ground-based Observations of Trans-Neptunian Objects Using a Single-epoch Parallax Measurement from L2

Space-based observatories at the second Sun–Earth Lagrange Point (L2) offer a unique opportunity to efficiently determine the orbits of distant solar system objects by taking advantage of the parallax effect that arises from nearly simultaneous ground- and space-based observations. Given the typical orbit of an observatory about L2, the observational baseline of ∼1.5 × 106 km between L2 and Earth results in an instantaneous parallax of ∼10″–100″, even for the most distant of detectable trans-Neptunian objects (TNOs) in our solar system. Current ground-based strategies for measuring the orbits of TNOs are very expensive and require multiple years of observations. We show that the direct constraint on the distance to a TNO, afforded by near-simultaneous ground- and space-based observations, allows us to confidently determine orbits with as few as three ground-based observations spanning 24 hr combined with a single observational epoch from an observatory orbiting L2.
Heterogeneous Criticality in High Frequency Finance: A Phase Transition in Flash Crashes

Flash crashes in financial markets have become increasingly important, attracting attention from financial regulators, market makers as well as from the media and the broader audience. Systemic risk and the propagation of shocks in financial markets is also a topic of great relevance that has attracted increasing attention in recent years. In the present work, we bridge the gap between these two topics with an in-depth investigation of the systemic risk structure of co-crashes in high frequency trading. We find that large co-crashes are systemic in their nature and differ from small ones. We demonstrate that there is a phase transition between co-crashes of small and large sizes, where the former involves mostly illiquid stocks, while large and liquid stocks are the most represented and central in the latter. This suggests that systemic effects and shock propagation might be triggered by simultaneous withdrawals or movement of liquidity by HFTs, arbitrageurs and market makers with cross-asset exposures.
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set

Post-hoc global/local feature attribution methods are progressively being employed to understand the decisions of complex machine learning models. Yet, because of limited amounts of data, it is possible to obtain a diversity of models with good empirical performance but that provide very different explanations for the same prediction, making it hard to derive insight from them. In this work, instead of aiming at reducing the under-specification of model explanations, we fully embrace it and extract logical statements about feature attributions that are consistent across all models with good empirical performance (i.e. all models in the Rashomon Set). We show that partial orders of local/global feature importance arise from this methodology enabling more nuanced interpretations by allowing pairs of features to be incomparable when there is no consensus on their relative importance. We prove that every relation among features present in these partial orders also holds in the rankings provided by existing approaches. Finally, we present three use cases employing hypothesis spaces with tractable Rashomon Sets (Additive models, Kernel Ridge, and Random Forests) and show that partial orders allow one to extract consistent local and global interpretations of models despite their under-specification.
Event-Triggered Consensus of Matrix-Weighted Networks Subject to Actuator Saturation

This paper examines the event-triggered global consensus of matrix-weighted networks subject to actuator saturation. A distributed protocol design is proposed for this category of networks to guarantee its global consensus subject to both event-triggered communication and actuator saturation. It is shown that the largest singular value of matrix-valued edge weights plays a crucial role in both protocol design and network performance, which renders the proposed framework more general than existing results that are only applicable to scalar-weighted networks. Conditions under which the global consensus can be guaranteed for leaderless matrix-weighted multi-agent networks are derived. However, the average consensus on the initial agents' states cannot be achieved due to the nonlinearities introduced in the closed-loop dynamics by the actuator saturation constraint. We further examine the scenario of leader-follower consensus for matrix-weighted multi-agent networks under the constraints of both event-triggered communication and actuator saturation. The applicability of the proposed protocol design framework to time-varying matrix-weighted networks is also examined. It is shown that the Zeno phenomenon can be excluded under the proposed interaction protocols. Simulation results in the context of the bearing-only cooperative formation of multi-vehicle systems are provided to demonstrate the effectiveness of theoretical results.
Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias

The generalization mystery of overparametrized deep nets has motivated efforts to understand how gradient descent (GD) converges to low-loss solutions that generalize well. Real-life neural networks are initialized from small random values and trained with cross-entropy loss for classification (unlike the"lazy"or"NTK"regime of training where analysis was more successful), and a recent sequence of results (Lyu and Li, 2020; Chizat and Bach, 2020; Ji and Telgarsky, 2020) provide theoretical evidence that GD may converge to the"max-margin"solution with zero loss, which presumably generalizes well. However, the global optimality of margin is proved only in some settings where neural nets are infinitely or exponentially wide. The current paper is able to establish this global optimality for two-layer Leaky ReLU nets trained with gradient flow on linearly separable and symmetric data, regardless of the width. The analysis also gives some theoretical justification for recent empirical findings (Kalimeris et al., 2019) on the so-called simplicity bias of GD towards linear or other"simple"classes of solutions, especially early in training. On the pessimistic side, the paper suggests that such results are fragile. A simple data manipulation can make gradient flow converge to a linear classifier with suboptimal margin.