Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of soil wetness from multiple models over Australia with observations

The McArthur Forest Fire Danger Index used in Australia for operational fire warnings has a component representing fuel availability called the Drought Factor (DF). The DF is partly based on soil moisture deficit, calculated as either the Keetch‐Byram Drought Index (KBDI) or Mount's Soil Dryness Index (MSDI). The KBDI and MSDI are simplified water balance models driven by observation based daily rainfall and temperature. In this work, gridded KBDI and MSDI analyses are computed at a horizontal resolution of 5 km and are verified against in‐situ soil moisture observations. Also verified is another simple model called the Antecedent Precipitation Index (API). Soil moisture analyses from the Australian Community Climate and Earth System Simulator (ACCESS) global Numerical Weather Prediction (NWP) system as well as remotely sensed soil wetness retrievals from the Advanced Scatterometer (ASCAT) are also verified. The verification shows that the NWP soil wetness analyses have greater skill and smaller biases than the KBDI, MSDI and API analyses. This is despite the NWP system having a coarse horizontal resolution and not using observed precipitation. The average temporal correlations (root mean square difference) between cosmic ray soil moisture monitoring facility observations and modeled or remotely sensed soil wetness are 0.82 (0.15 ±0.02), 0.66 (0.33 ±0.07), 0.77 (0.20 ±0.03), 0.74 (0.22 ±0.03) and 0.83 (0.18 ±0.04) for NWP, KBDI, MSDI, API and ASCAT. The results from this study suggests that analyses of soil moisture can be greatly improved by using physically based land surface models, remote sensing measurements and data assimilation.
Policy capacities for new regional industrial path development – The case of new media and biogas in southern Sweden

Over the past few years, a growing body of work in economic geography and innovation studies has enhanced our understanding of forms and determinants of regional industrial path development. The importance of policy, however, has received limited attention and accordingly, the role of policy for the emergence and development of new regional industrial growth paths remains largely unexplored. This paper takes an institutional perspective and suggests that the regional innovation system approach can contribute to conceptualising and analysing the role of policy for new regional industrial path development. We argue that in order to turn regional preconditions into new growth paths, regional innovation systems require strong policy capacities, consisting of formal and governance capacities. In the empirical part, we analyse the emergence and further development of two new growth paths in the region of Scania in southern Sweden, namely biogas and new media. Based on personal interviews with policy makers, representatives from knowledge and supporting organisations and firms as well as a document analysis, we investigate how policy interventions have influenced the rise and evolution of these two industries. We show that in both cases, policy-led initiatives have played an important role in enabling new path development. We find that policy can play multiple roles in nurturing and maintaining new growth paths and that these are closely interlinked with particular policy capacities of regional innovation systems.
THE SPECTRUM OF THE LAPLACIAN ON Γ \ H THE TRACE FORMULA

Maaß cusp forms on PSL(2,Z)\H are the basic building blocks of automorphic forms on GL(2). Yet, their existence is known almost entirely through indirect means; no one has found a single example of an everywhere-unramified cuspidal Maaß form for PSL(2,Z). The spectral theory of PSL(2,Z)\H is deeply important and remains exceedingly mysterious. In this expository article, we introduce some of the key questions in this area, and the main tool used to study them, the Selberg trace formula.
Combining Experimental and Observational Data to Estimate Treatment Effects on Long Term Outcomes

There has been an increase in interest in experimental evaluations to estimate causal effects, partly because their internal validity tends to be high. At the same time, as part of the big data revolution, large, detailed, and representative, administrative data sets have become more widely available. However, the credibility of estimates of causal effects based on such data sets alone can be low. In this paper, we develop statistical methods for systematically combining experimental and observational data to obtain credible estimates of the causal effect of a binary treatment on a primary outcome that we only observe in the observational sample. Both the observational and experimental samples contain data about a treatment, observable individual characteristics, and a secondary (often short term) outcome. To estimate the effect of a treatment on the primary outcome while addressing the potential confounding in the observational sample, we propose a method that makes use of estimates of the relationship between the treatment and the secondary outcome from the experimental sample. If assignment to the treatment in the observational sample were unconfounded, we would expect the treatment effects on the secondary outcome in the two samples to be similar. We interpret differences in the estimated causal effects on the secondary outcome between the two samples as evidence of unobserved confounders in the observational sample, and develop control function methods for using those differences to adjust the estimates of the treatment effects on the primary outcome. We illustrate these ideas by combining data on class size and third grade test scores from the Project STAR experiment with observational data on class size and both third and eighth grade test scores from the New York school system.
Learning What to Defer for Maximum Independent Sets

Designing efficient algorithms for combinatorial optimization appears ubiquitously in various scientific fields. Recently, deep reinforcement learning (DRL) frameworks have gained considerable attention as a new approach: they can automate the design of a solver while relying less on sophisticated domain knowledge of the target problem. However, the existing DRL solvers determine the solution using a number of stages proportional to the number of elements in the solution, which severely limits their applicability to large-scale graphs. In this paper, we seek to resolve this issue by proposing a novel DRL scheme, coined learning what to defer (LwD), where the agent adaptively shrinks or stretch the number of stages by learning to distribute the element-wise decisions of the solution at each stage. We apply the proposed framework to the maximum independent set (MIS) problem, and demonstrate its significant improvement over the current state-of-the-art DRL scheme. We also show that LwD can outperform the conventional MIS solvers on large-scale graphs having millions of vertices, under a limited time budget.
The $L^1$-contraction principle in optimal transport

In this work, we use the JKO scheme to approximate a general class of diffusion problems generated by Darcy's law. Although the scheme is now classical, if the energy density is spatially inhomogeneous or irregular, many standard methods fail to apply to establish convergence in the continuum limit. To overcome these difficulties, we analyze the scheme through its dual problem and establish a novel $L^1$-contraction principle for the density variable. Notably, the contraction principle relies only on the existence of an optimal transport map and the convexity structure of the energy. As a result, the principle holds in a very general setting, and opens the door to using optimal-transport-based variational schemes to study a larger class of non-linear inhomogeneous parabolic equations.
Can third-party observers detect the emotional rewards of generous spending?

Can others detect the emotional consequences of our personal choices? Here, we investigate whether third-party observers can detect the emotional benefits of two factors shown to influence self-reported happiness: the speed with which people make decisions and the generosity of spending choices. Participants were randomly assigned to purchase a goody bag either for themselves or for a sick child, and to choose the contents of this goody bag either as quickly as possible, or by taking as much time as needed. Then, participants reported their current emotional state and were rated for happiness by a research assistant blind to their spending condition. Analyses revealed that purchasing a gift for someone else not only improved participants’ self-reported mood, but that observers could detect these affective differences as well. Observers also rated participants who made their spending decision more quickly as happier, although participants did not report these emotional differences.
Parameterized MDPs and Reinforcement Learning Problems—A Maximum Entropy Principle-Based Framework

We present a framework to address a class of sequential decision-making problems. Our framework features learning the optimal control policy with robustness to noisy data, determining the unknown state and action parameters, and performing sensitivity analysis with respect to problem parameters. We consider two broad categories of sequential decision-making problems modeled as infinite horizon Markov decision processes (MDPs) with (and without) an absorbing state. The central idea underlying our framework is to quantify exploration in terms of the Shannon entropy of the trajectories under the MDP and determine the stochastic policy that maximizes it while guaranteeing a low value of the expected cost along a trajectory. This resulting policy enhances the quality of exploration early on in the learning process, and consequently allows faster convergence rates and robust solutions even in the presence of noisy data as demonstrated in our comparisons to popular algorithms, such as <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-learning, Double <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-learning, and entropy regularized Soft <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-learning. The framework extends to the class of parameterized MDP and RL problems, where states and actions are parameter dependent, and the objective is to determine the optimal parameters along with the corresponding optimal policy. Here, the associated cost function can possibly be nonconvex with multiple poor local minima. Simulation results applied to a 5G small cell network problem demonstrate the successful determination of communication routes and the small cell locations. We also obtain sensitivity measures to problem parameters and robustness to noisy environment data.
Determinants of the Real Impact of Banking Crises: A Review and New Evidence

We examine which variables are robust in explaining cross-country differences in the real impact of systemic banking crises. Based on a meta-analysis, we identify 21 variables frequently used as determinants of the severity of crises. Employing nine proxies for crisis severity, we find that large current account imbalances are the most robust determinant of the real impact of banking crises. Countries with a high GDP per capita have more prolonged downfalls after the occurrence of a banking crisis. Exchange rate developments and pre-crisis GDP growth are related to the peak-to-trough impact of a banking crisis.
The Gaia-ESO Survey: membership probabilities for stars in 32 open clusters from 3D kinematics

The Gaia-ESO Survey (GES) observed many open clusters as part of its programme to spectroscopically characterize the various Milky Way populations. GES spectroscopy and Gaia astrometry from its second data release are used here to assign membership probabilities to targets towards 32 open clusters with ages from 1 to 3800 Myr, based on maximum likelihood modelling of the 3D kinematics of the cluster and field populations. From a parent catalogue of 14 398 individual targets, 5032 stars with uniformly determined 3D velocities, Teff, log g, and chemistry are assigned cluster membership with probability >0.9, and with an average probability of 0.991. The robustness of the membership probabilities is demonstrated using independent membership criteria (lithium and parallax) in two of the youngest clusters. The addition of radial velocities improves membership discrimination over proper motion selection alone, especially in more distant clusters. The kinematically selected nature of the membership lists, independent of photometry and chemistry, makes the catalogue a valuable resource for testing stellar evolutionary models and investigating the time evolution of various parameters.
Effects of Cultivar, Nitrogen Rate, and Planting Density on Rice-Grain Quality

To achieve superior rice-grain quality, more emphasis has been placed on the genetic diversity of breeding programs, although this improvement could be seriously restricted in the absence of comparable agricultural management practices. Nitrogen (N) application and planting density are two important agronomic practices influencing rice growth, yield, and grain quality. This study investigated the four main aspects of rice-grain quality, namely, milling (brown-rice, milled-rice, and head-rice percentage), appearance (length/width ratio, chalky-kernel percentage, and chalkiness), nutrition (protein content), and cooking and eating quality (apparent amylose content, gel consistency, and pasting viscosities) of two rice cultivars (Shendao 47 and Jingyou 586) under four N rates (0, 140, 180, and 220 kg ha−1), and three planting densities (25 × 104, 16.7 × 104, and 12.5 × 104 hills ha−1) in a field trial from 2015 to 2016. The four main aspects of rice-grain quality were significantly influenced by cultivar. Several aspects were affected by the interactions of N rate and cultivar. No significant interaction between N rate and plating density was detected for all grain-quality parameters. A higher N rate increased the percentages of brown rice and head rice, chalky-kernel percentage, and setback and peak time values, but reduced the length/width ratio, chalkiness, apparent amylose content, gel consistency, and peak-, trough-, and final-viscosity values. These results indicate that the N rate has a beneficial effect on milling and nutritional quality, but a detrimental effect on appearance and cooking and eating quality. Jingyou 586 and Shendao 47 had different responses to planting density in terms of grain quality. Our study indicates that low planting density for Jingyou 586, but a medium one for Shendao 47, is favorable for grain quality.
Forbidden line diagnostics of photoevaporative disc winds

Photoevaporation driven by high energy radiation from the central star plays an important role in the evolution of protoplanetary discs. Photoevaporative winds have been unambiguously detected through blue-shifted emission lines, but their detailed properties remain uncertain. Here we present a new empirical approach to make observational predictions of these thermal winds, seeking to fill the gap between theory and observations. We use a self-similar model of an isothermal wind to compute line profiles of several characteristic emission lines (the [Ne${\rm{\scriptsize II}}$] line at 12.81 $\mu$m, and optical forbidden lines such as [O${\rm{\scriptsize I}}$] 6300 $\mathring{A}$ and [S${\rm{\scriptsize II}}$] 4068/4076 $\mathring{A}$), studying how the lines are affected by parameters such as the gas temperature, disc inclinations, and density profile. Our model successfully reproduces blue-shifted lines with $v_{\rm peak} \lesssim 10$ km/s, which decrease with increasing disc inclination. The line widths increase with increasing disc inclinations and range from $\Delta v \sim 15-30$ km/s. The predicted blue-shifts are mostly sensitive to the gas sound speed. The observed [Ne${\rm{\scriptsize II}}$] line profiles are consistent with a thermal wind and point towards a relatively high sound speed, as expected for EUV photoevaporation. However, the observed [O${\rm{\scriptsize I}}$] line profiles require lower temperatures, as expected in X-ray photoevaporation, and show a wider scatter that is difficult to reconcile with a single wind model; it seems likely that these lines trace different components of a multi-phase wind. We also note that the spectral resolution of current observations remains an important limiting factor in these studies, and that higher resolution spectra are required if emission lines are to further our understanding of protoplanetary disc winds.
Punish One, Teach A Hundred: The Sobering Effect of Punishment on the Unpunished

Direct experience of a peer’s punishment might make non-punished peers reassess the probability and consequences of facing punishment and hence induce a change in their behavior. We test this mechanism in a setting, China, in which we observe the reactions to the same peer’s punishment by listed firms with different incentives to react - state-owned enterprises (SOEs) and non-SOEs. After observing peers punished for wrongdoing in loan guarantees to related parties, SOEs - which are less disciplined by traditional governance mechanisms than non-SOEs - cut their loan guarantees. SOEs whose CEOs have stronger career concerns react more than other SOEs to the same punishment events, a result that systematic differences between SOEs and non-SOEs cannot drive. SOEs react more to events with higher press coverage even if information about all events is publicly available. After peers' punishments, SOEs also increase their board independence, reduce inefficient investment, increase total factor productivity, and experience positive cumulative abnormal returns. The bank debt and investment of related parties that benefited from tunneling drop after listed peers’ punishments. Strategic punishments could be a cost-effective governance mechanism when other forms of governance are ineffective.
Understanding Global Loss Landscape of One-hidden-layer ReLU Networks, Part 1: Theory

For one-hidden-layer ReLU networks, we prove that all differentiable local minima are global inside differentiable regions. We give the locations and losses of differentiable local minima, and show that these local minima can be isolated points or continuous hyperplanes, depending on an interplay between data, activation pattern of hidden neurons and network size. Furthermore, we give necessary and sufficient conditions for the existence of saddle points as well as non-differentiable local minima, and their locations if they exist.
Visor: Privacy-Preserving Video Analytics as a Cloud Service

Video-analytics-as-a-service is becoming an important offering for cloud providers. A key concern in such services is privacy of the videos being analyzed. While trusted execution environments (TEEs) are promising options for preventing the direct leakage of private video content, they remain vulnerable to side-channel attacks. We present Visor, a system that provides confidentiality for the user's video stream as well as the ML models in the presence of a compromised cloud platform and untrusted co-tenants. Visor executes video pipelines in a hybrid TEE that spans both the CPU and GPU. It protects the pipeline against side-channel attacks induced by data-dependent access patterns of video modules, and also addresses leakage in the CPU-GPU communication channel. Visor is up to $1000\times$ faster than naive oblivious solutions, and its overheads relative to a non-oblivious baseline are limited to $2\times$--$6\times$.
3D Placement for Multi-UAV Relaying: An Iterative Gibbs-Sampling and Block Coordinate Descent Optimization Approach

In this paper, we consider an unmanned aerial vehicle (UAV) enabled relaying system where multiple UAVs are deployed as aerial relays to support simultaneous communications from a set of source nodes to their destination nodes on the ground. An optimization problem is formulated under practical channel models to maximize the minimum achievable expected rate among all pairs of ground nodes by jointly designing UAVs’ three-dimensional (3D) placement as well as the bandwidth-and-power allocation. This problem, however, is non-convex and thus difficult to solve. As such, we propose a new method, called iterative Gibbs-sampling and block- coordinate-descent (IGS-BCD), to efficiently obtain a high-quality suboptimal solution by synergizing the advantages of both the deterministic (BCD) and stochastic (GS) optimization methods. Specifically, our proposed method alternates between two optimization phases until convergence is reached, namely, one phase that uses the BCD method to find locally-optimal UAVs’ 3D placement and the other phase that leverages the GS method to generate new UAVs’ 3D placement for exploration. Moreover, we present an efficient method for properly initializing UAVs’ placement that leads to faster convergence of the proposed IGS-BCD algorithm. Numerical results show that the proposed IGS-BCD and initialization methods outperform the conventional BCD or GS method alone in terms of convergence-and-performance trade-off, as well as other benchmark schemes.
Bayesian estimation of sparse signals with a continuous spike-and-slab prior

We introduce a new framework for estimation of sparse normal means, bridging the gap between popular frequentist strategies (LASSO) and popular Bayesian strategies (spike-and-slab). The main thrust of this paper is to introduce the family of Spike-and-Slab LASSO (SS-LASSO) priors, which form a continuum between the Laplace prior and the point-mass spike-and-slab prior. We establish several appealing frequentist properties of SS-LASSO priors, contrasting them with these two limiting cases. First, we adopt the penalized likelihood perspective on Bayesian modal estimation and introduce the framework of Bayesian penalty mixing with spike-and-slab priors. We show that the SS-LASSO global posterior mode is (near) minimax rate-optimal under squared error loss, similarly as the LASSO. Going further, we introduce an adaptive two-step estimator which can achieve provably sharper performance than the LASSO. Second, we show that the whole posterior keeps pace with the global mode and concentrates at the (near) minimax rate, a property that is known not to hold for the single Laplace prior. The minimax-rate optimality is obtained with a suitable class of independent product priors (for known levels of sparsity) as well as with dependent mixing priors (adapting to the unknown levels of sparsity). Up to now, the rate-optimal posterior concentration has been established only for spike-and-slab priors with a point mass at zero. Thus, the SS-LASSO priors, despite being continuous, possess similar optimality properties as the “theoretically ideal” point-mass mixtures. These results provide valuable theoretical justiﬁcation for our proposed class of priors, underpinning their intuitive appeal and practical potential.
Towards exact descriptions of scaling prefactors in finite dimensional jamming

No known analytic framework precisely explains all the phenomena observed in jamming. The replica theory for glass and jamming is a mean field theory which attempts to do so by working in the limit of infinite dimensions, such that correlations between neighbors are negligible. As such, results from this mean field theory are not guaranteed to be observed in finite dimensions. However, many results in mean field for jamming have been shown to be exact or nearly exact in low dimensions. This suggests that the infinite dimensional limit is not necessary to obtain these results. In this paper, we perform precision measurements of jamming scaling relationships between pressure, excess packing fraction, and number of excess contacts from dimensions 2-10 in order to extract the prefactors to these scalings. While these prefactors should be highly sensitive to finite dimensional corrections, we find the mean field predictions for these prefactors to be exact in low dimensions. Thus the mean field approximation is not necessary for deriving these prefactors. We present an exact, first principles derivation for one, leaving the other as an open question.