Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing model uncertainty of climate change impacts on high latitude carbon assimilation

The Arctic–Boreal Region (ABR) has a large impact on global vegetation–atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of CO2. The ABR is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models such that reducing this uncertainty is critical for more accurately estimating global carbon cycling and understanding the response of the region to global change. Process representation and parameterization associated with gross primary productivity (GPP) drives a large amount of this model uncertainty, particularly within the next 50 years, where the response of existing vegetation to climate change will dominate estimates of GPP for the region. Here we review our current understanding and model representation of GPP in northern latitudes, focusing on vegetation composition, phenology, and physiology, and consider how climate change alters these three components. We highlight challenges in the ABR for predicting GPP, but also focus on the unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots‐on‐the‐ground science.
Unsupervised Source Separation By Steering Pretrained Music Models

We showcase an unsupervised method that repurposes deep models trained for music generation and music tagging for audio source separation, without any retraining. An audio generation model is conditioned on an input mixture, producing a latent encoding of the audio used to generate audio. This generated audio is fed to a pretrained music tagger that creates source labels. The cross-entropy loss between the tag distribution for the generated audio and a predefined distribution for an isolated source is used to guide gradient ascent in the (unchanging) latent space of the generative model. This system does not update the weights of the generative model or the tagger, and only relies on moving through the generative model's latent space to produce separated sources. We use OpenAI's Jukebox as the pretrained generative model, and we couple it with four kinds of pretrained music taggers (two architectures and two tagging datasets). Experimental results on two source separation datasets, show this approach can produce separation estimates for a wider variety of sources than any tested supervised or unsupervised system. This work points to the vast and heretofore untapped potential of large pretrained music models for audio-to-audio tasks like source separation.
On the Tractability of Neural Causal Inference

Roth (1996) proved that any form of marginal inference with probabilistic graphical models (e.g. Bayesian Networks) will at least be NP-hard. Introduced and extensively investigated in the past decade, the neural probabilistic circuits known as sum-product network (SPN) oﬀers linear time complexity. On another note, research around neural causal models (NCM) recently gained traction, demanding a tighter integration of causality for machine learning. To this end, we present a theoretical investigation of if, when, how and under what cost tractability occurs for diﬀerent NCM. We prove that SPN-based causal inference is generally tractable, opposed to standard MLP-based NCM. We further introduce a new tractable NCM-class that is eﬃcient in inference and fully expressive in terms of Pearl’s Causal Hierarchy. Our comparative empirical illustration on simulations and standard benchmarks validates our theoretical proofs.
A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses for Administrative Tax Data

Federal administrative tax data are invaluable for research, but because of privacy concerns, access to these data is typically limited to select agencies and a few individuals. An alternative to sharing microlevel data are validation servers, which allow individuals to query statistics without accessing the confidential data. This paper studies the feasibility of using differentially private (DP) methods to implement such a server. We provide an extensive study on existing DP methods for releasing tabular statistics, means, quantiles, and regression estimates. We also include new methodological adaptations to existing DP regression algorithms for using new data types and returning standard error estimates. We evaluate the selected methods based on the accuracy of the output for statistical analyses, using real administrative tax data obtained from the Internal Revenue Service Statistics of Income (SOI) Division. Our findings show that a validation server would be feasible for simple statistics but would struggle to produce accurate regression estimates and confidence intervals. We outline challenges and offer recommendations for future work on validation servers. This is the first comprehensive statistical study of DP methodology on a real, complex dataset, that has significant implications for the direction of a growing research field.
Scalable Smartphone Cluster for Deep Learning

Various deep learning applications on smartphones have been rapidly rising, but training deep neural networks (DNNs) has too large computational burden to be executed on a single smartphone. A portable cluster, which connects smartphones with a wireless network and supports parallel computation using them, can be a potential approach to resolve the issue. However, by our findings, the limitations of wireless communication restrict the cluster size to up to 30 smartphones. Such small-scale clusters have insufficient computational power to train DNNs from scratch. In this paper, we propose a scalable smartphone cluster enabling deep learning training by removing the portability to increase its computational efficiency. The cluster connects 138 Galaxy S10+ devices with a wired network using Ethernet. We implemented large-batch synchronous training of DNNs based on Caffe, a deep learning library. The smartphone cluster yielded 90% of the speed of a P100 when training ResNet-50, and approximately 43x speed-up of a V100 when training MobileNet-v1.
Policy Search using Dynamic Mirror Descent MPC for Model Free Off Policy RL

Recent works in Reinforcement Learning (RL) combine model-free (Mf)-RL algorithms with model-based (Mb)-RL approaches to get the best from both: asymptotic performance of Mf-RL and high sample-efficiency of Mb-RL. Inspired by these works, we propose a hierarchical framework that integrates online learning for the Mb-trajectory optimization with off-policy methods for the Mf-RL. In particular, two loops are proposed, where the Dynamic Mirror Descent based Model Predictive Control (DMD-MPC) is used as the inner loop to obtain an optimal sequence of actions. These actions are in turn used to significantly accelerate the outer loop Mf-RL. We show that our formulation is generic for a broad class of MPC based policies and objectives, and includes some of the well-known Mb-Mf approaches. Based on the frame work we define two algorithms to increase sample efficiency of Off Policy RL and to guide end to end RL algorithms for online adaption respectively. Thus we finally introduce two novel algorithms: DynamicMirror Descent Model Predictive RL (DeMoRL), which uses the method of elite fractions for the inner loop and Soft Actor-Critic (SAC) as the off-policy RL for the outer loop and Dynamic-Mirror Descent Model Predictive Layer (DeMo Layer), a special case of hierarchical framework which guides linear policies trained using Augmented Random Search(ARS). Our experiments show faster convergence of the proposed DeMo RL, and better or equal performance compared to other Mf-Mb approaches on benchmark MuJoCo control tasks. The DeMo Layer was tested on classical Cartpole and custom built Quadruped trained using Linear Policy Approach. The results shows that DeMo Layer significantly increases performance of the Linear Policy in both the settings.
Multi-task Recurrent Neural Networks to Simultaneously Infer Mode and Purpose in GPS Trajectories

Multi-task learning is assumed as a powerful inference method, specifically, where there is a considerable correlation between multiple tasks, predicting them in an unique framework may enhance prediction results. This research challenges this assumption by developing several single-task models to compare their results against multi-task learners to infer mode and purpose of trip from smartphone travel survey data collected as part of a smartphone-based travel survey. GPS trajectory data along with socio-demographics and destination-related characteristics are fed into a multi-input neural network framework to predict two outputs; mode and purpose. We deployed Recurrent Neural Networks (RNN) that are fed by sequential GPS trajectories. To process the socio-demographics and destination-related characteristics, another neural network, with different embedding and dense layers is used in parallel with RNN layers in a multi-input multi-output framework. The results are compared against the single-task learners that classify mode and purpose independently. We also investigate different RNN approaches such as Long-Short Term Memory (LSTM), Gated Recurrent Units (GRU) and Bi-directional Gated Recurrent Units (Bi-GRU). The best multi-task learner was a Bi-GRU model able to classify mode and purpose with an F1-measures of 84.33% and 78.28%, while the best single-task learner to infer mode of transport was a GRU model that achieved an F1-measure of 86.50%, and the best single-task Bi-GRU purpose detection model that reached an F1-measure of 77.38%. While there's an assumption of higher performance of multi-task over sing-task learners, the results of this study does not hold such an assumption and shows, in the context of mode and trip purpose inference from GPS trajectory data, a multi-task learning approach does not bring any considerable advantage over single-task learners.
Neptune's ring arcs from VLT/SPHERE-IRDIS near-infrared observations

Context. Neptune’s incomplete ring arcs have been stable since their discovery in 1984 by stellar occultation. Although these struc- tures should be destroyed within a few months through di ﬀ erential Keplerian motion, imaging data over the past couple of decades have shown that these structures remain stable. Aims. We present the ﬁrst SPHERE near-infrared observations of Neptune’s ring arcs taken at 2.2 µ m (broadband Ks) with the IRDIS camera at the Very Large Telescope (VLT) in August 2016. Methods. The images were aligned using the ephemerides of the satellite Proteus and were suitably co-added to enhance ring and satellite signals. Results. We analyse high-angular-resolution near-infrared images of Neptune’s ring arcs obtained in 2016 at the ESO VLT-UT3 with the adaptive-optics-fed camera SPHERE-IRDIS. We derive accurate mean motion values for the arcs and the nearby satellite Galatea. The trailing arcs Fraternité and Égalité have been stable since they were last observed in 2007. Furthermore, we conﬁrm the fading away of the leading arcs Courage and Liberté. Finally, we conﬁrm the mismatch between the arcs’ position and the 42:43 inclined and eccentric corotation resonances with Galatea, thus demonstrating that no 42:43 corotation model works to explain the azimuthal conﬁnement of the arcs’ materiel.
Functional instrumental variable regression with an application to estimating the impact of immigration on native wages

Functional linear regression gets its popularity as a statistical tool to study the relationship between function-valued response and exogenous explanatory variables. However, in practice, it is hard to expect that the explanatory variables of interest are perfectly exogenous, due to, for example, the presence of omitted variables and measurement error. Despite its empirical relevance, it was not until recently that this issue of endogeneity was studied in the literature on functional regression, and the development in this direction does not seem to sufficiently meet practitioners' needs; for example, this issue has been discussed with paying particular attention on consistent estimation and thus distributional properties of the proposed estimators still remain to be further explored. To fill this gap, this paper proposes new consistent FPCA-based instrumental variable estimators and develops their asymptotic properties in detail. Simulation experiments under a wide range of settings show that the proposed estimators perform considerably well. We apply our methodology to estimate the impact of immigration on native wages.
The MASSIVE Survey. XVI. The Stellar Initial Mass Function in the Center of MASSIVE Early-type Galaxies

The stellar initial mass function (IMF) is a fundamental property in the measurement of stellar masses and galaxy star formation histories. In this work, we focus on the most massive galaxies in the nearby universe log(M⋆/M⊙)>11.2 . We obtain high-quality Magellan/LDSS-3 long-slit spectroscopy with a wide wavelength coverage of 0.4–1.01 μm for 41 early-type galaxies (ETGs) in the MASSIVE survey and derive high signal-to-noise spectra within an aperture of R e/8. Using detailed stellar synthesis models, we constrain the elemental abundances and stellar IMF of each galaxy through full spectral modeling. All the ETGs in our sample have an IMF that is steeper than a Milky Way (Kroupa) IMF. The best-fit IMF mismatch parameter, α IMF = (M/L)/(M/L)MW, ranges from 1.1 to 3.1, with an average of 〈α IMF〉 = 1.84, suggesting that on average, the IMF is more bottom heavy than Salpeter. Comparing the estimated stellar masses with the dynamical masses, we find that most galaxies have stellar masses that are smaller than their dynamical masses within the 1σ uncertainty. We complement our sample with lower-mass galaxies from the literature and confirm that log(αIMF) is positively correlated with log(σ) , log(M⋆) , and log(Mdyn) . From the combined sample, we show that the IMF in the centers of more massive ETGs is more bottom heavy. In addition, we find that log(αIMF) is positively correlated with both [Mg/Fe] and the estimated total metallicity [Z/H]. We find suggestive evidence that the effective stellar surface density ΣKroupa might be responsible for the variation of α IMF. We conclude that σ, [Mg/Fe], and [Z/H] are the primary drivers of the global stellar IMF variation.
Molecular Chemistry for Dark Matter. II. Recombination, Molecule Formation, and Halo Mass Function in Atomic Dark Matter

Dissipative dark matter predicts rich observable phenomena that can be tested with future large-scale structure surveys. As a specific example, we study atomic dark matter, consisting of a heavy particle and a light particle charged under a dark electromagnetism. In particular, we calculate the cosmological evolution of atomic dark matter focusing on dark recombination and dark molecule formation. We have obtained the relevant interaction rate coefficients by rescaling the rates for normal hydrogen, and evolved the abundances for ionized, atomic, and molecular states using a modified version of Recfast++ (which we have released publicly at a a https://github.com/jamesgurian/RecfastJulia ). We also provide an analytical approximation for the final abundances. We then calculate the effects of atomic dark matter on the linear power spectrum, which enter through a dark photon diffusion and dark acoustic oscillations. At formation time, the atomic dark matter model suppresses halo abundances on scales smaller than the diffusion scale, just as warm dark matter models suppress the abundance below the free-streaming scale. The subsequent evolution with radiative cooling, however, will alter the halo mass function further. https://github.com/jamesgurian/RecfastJulia
A Model Earth-sized Planet in the Habitable Zone of α Centauri A/B

The bulk chemical composition and interior structure of rocky exoplanets are fundamentally important to understand their long-term evolution and potential habitability. Observations of the chemical compositions of solar system rocky bodies and of other planetary systems have increasingly shown a concordant picture that the chemical composition of rocky planets reflects that of their host stars for refractory elements, whereas this expression breaks down for volatiles. This behavior is explained by devolatilization during planetary formation and early evolution. Here we apply a devolatilization model calibrated with solar system bodies to the chemical composition of our nearest Sun-like stars—α Centauri A and B—to estimate the bulk composition of any habitable-zone rocky planet in this binary system (“α-Cen-Earth”). Through further modeling of likely planetary interiors and early atmospheres, we find that, compared to Earth, such a planet is expected to have (i) a reduced (primitive) mantle that is similarly dominated by silicates, albeit enriched in carbon-bearing species (graphite/diamond); (ii) a slightly larger iron core, with a core mass fraction of 38.4−5.1+4.7 wt% (see Earth’s 32.5 ± 0.3 wt%); (iii) an equivalent water-storage capacity; and (iv) a CO2–CH4–H2O-dominated early atmosphere that resembles that of Archean Earth. Further taking into account its ∼25% lower intrinsic radiogenic heating from long-lived radionuclides, an ancient α-Cen-Earth (∼1.5–2.5 Gyr older than Earth) is expected to have less efficient mantle convection and planetary resurfacing, with a potentially prolonged history of stagnant-lid regimes.
Constraints on small-scale primordial density fluctuation from cosmic microwave background through dark matter annihilation

The cosmic microwave background (CMB) observation by the Planck satellite precisely determines primordial curvature fluctuations on larger scales than 𝒪(1) Mpc, while the small-scale curvature fluctuation is still less constrained. The constraint on small-scale fluctuations is highly improved if we assume the standard thermal relic dark matter scenario. When small-scale fluctuations are large enough, dense regions collapse to form small halos even in a redshift z ≳ 103, which is called “ultracompact minihalos”. These minihalos enhance the annihilation of the dark matter and it is constrained by observations such as extragalactic gamma rays and the CMB. We revisit the effect of minihalos formed by the small-scale density fluctuations and calculate the ionization history modified by the dark matter annihilation assuming the thermally produced WIMP accounting for all dark matter with e + e −-channel annihilation. We perform the Markov Chain Monte Carlo method to constrain the size of small-scale curvature fluctuations by the CMB power spectrum. It is found that the constraint from the CMB power spectrum is comparable to that from the extragalactic gamma rays. Assuming a pair-annihilation cross section 〈σ v〉 = 3 × 10-26cm3/s and a mass of dark matter 1 TeV, we constrain the curvature power spectrum 𝒫ℛ(k) < 𝒪(10-6) over 1 Mpc-1 < k < 106 Mpc-1. We confirm that our constraint mainly comes from the energy injection in early time (z ≳ 100) and hence it is independent of the uncertainty of minihalo properties in the late time.
Intrinsic and Extrinsic Anomalous Hall Effects in Disordered Magnetic Weyl Semimetal

We study the intrinsic and extrinsic Hall effects in disordered magnetic Weyl semimetals numerically. We show that in Weyl metals, where the Fermi energy deviates from the Weyl point, the Hall and longitudinal conductances exhibit a specific relation, which is distinguished from the well-known relation in integer quantum Hall systems. Around the Weyl point, the Hall conductance increases with increasing longitudinal conductance. This increasing behavior indicates the existence of additional contributions to the Hall conductance from the subbands of Weyl cones besides that from the bulk Berry curvature. We also show that the extrinsic anomalous Hall effect due to the spin scatterers (skew scattering) is significantly suppressed in Weyl metals.
CoVA: Context-aware Visual Attention for Webpage Information Extraction

Webpage information extraction (WIE) is an important step to create knowledge bases. For this, classical WIE methods leverage the Document Object Model (DOM) tree of a website. However, use of the DOM tree poses significant challenges as context and appearance are encoded in an abstract manner. To address this challenge we propose to reformulate WIE as a context-aware Webpage Object Detection task. Specifically, we develop a Context-aware Visual Attention-based (CoVA) detection pipeline which combines appearance features with syntactical structure from the DOM tree. To study the approach we collect a new large-scale datase of e-commerce websites for which we manually annotate every web element with four labels: product price, product title, product image and others. On this dataset we show that the proposed CoVA approach is a new challenging baseline which improves upon prior state-of-the-art methods.
Learning Stochastic Shortest Path with Linear Function Approximation

We study the stochastic shortest path (SSP) problem in reinforcement learning with linear function approximation, where the transition kernel is represented as a linear mixture of unknown models. We call this class of SSP problems as linear mixture SSPs. We propose a novel algorithm with Hoeffding-type confidence sets for learning the linear mixture SSP, which can attain an $\tilde{\mathcal{O}}(d B_{\star}^{1.5}\sqrt{K/c_{\min}})$ regret. Here $K$ is the number of episodes, $d$ is the dimension of the feature mapping in the mixture model, $B_{\star}$ bounds the expected cumulative cost of the optimal policy, and $c_{\min}>0$ is the lower bound of the cost function. Our algorithm also applies to the case when $c_{\min} = 0$, and an $\tilde{\mathcal{O}}(K^{2/3})$ regret is guaranteed. To the best of our knowledge, this is the first algorithm with a sublinear regret guarantee for learning linear mixture SSP. Moreover, we design a refined Bernstein-type confidence set and propose an improved algorithm, which provably achieves an $\tilde{\mathcal{O}}(d B_{\star}\sqrt{K/c_{\min}})$ regret. In complement to the regret upper bounds, we also prove a lower bound of $\Omega(dB_{\star} \sqrt{K})$. Hence, our improved algorithm matches the lower bound up to a $1/\sqrt{c_{\min}}$ factor and poly-logarithmic factors, achieving a near-optimal regret guarantee.
Godot is not coming: when we will let innovations enter psychiatry?

Current diagnostic practice in psychiatry is not relying on objective biophysical evidence. Recent pandemic emphasized the need to address the rising number of mood disorders (in particular, depression) cases in a more efficient way. We are proposing several already developed practices that can help improve that diagnostic process: detection based on electrophysiological signals (both electroencephalogram and electrocardiogram based) that were shown to be accurate for clinical practice and several modalities of electromagnetic stimulation that were proven to ameliorate symptoms of depression. In this work, we are connecting the two with explanations coming from physiological complexity studies (and our own work) as well as advanced statistical methods like machine learning and the Bayesian inference approach. It is shown that fractal and nonlinear measures can adequately quantify previously undetected changes in intrinsic dynamics of physiological systems, providing the basis for early detection of depression. We are also advocating for early screening of cardiovascular risks in depression which is in connection to previously described decomplexification of the autonomous nervous system resulting in symptoms recognized clinically. All that said, additional information about the level of complexity can help clinicians make a better decisions in the therapeutic process, increase the overall effectiveness of the treatment, and finally increase the quality of life of the patient.