Miracle-dz/UniFAR-FTUs huggingface dataset:
Observability for the Schr\"odinger Equation: an Optimal Transport Approach

We establish an observation inequality for the Schrödinger equation on R, uniform in the Planck constant h̵ ∈ [0,1]. The proof is based on the pseudometric introduced in [F. Golse, T. Paul, Arch. Rational Mech. Anal. 223 (2017), 57–94]. This inequality involves only effective constants which are computed explicitly in their dependence in h̵ and all parameters involved. 1. Observation inequality for the Schrödinger equation Consider the Schrödinger equation where the (real-valued) potential V belongs to C1,1(Rd) is such that the quantum Hamiltonian − 1 2 h̵∆y + V (y) has a self-adjoint extension on H ∶= L2(Rd): (1) ih̵∂tψ(t, y) = (− 12 h̵∆y + V (y))ψ(t, y) , ψ∣t=0 = ψ . In the equation above, h̵ > 0 the reduced Planck constant, and the particle mass is set to 1. An observation inequality for the Schrödinger equation (1) is an inequality of the form
Evaluation of New Submillimeter VLBI Sites for the Event Horizon Telescope

The Event Horizon Telescope (EHT) is a very long-baseline interferometer built to image supermassive black holes on event-horizon scales. In this paper, we investigate candidate sites for an expanded EHT array with improved imaging capabilities. We use historical meteorology and radiative transfer analysis to evaluate site performance. Most of the existing sites in the EHT array have median zenith opacity less than 0.2 at 230 GHz during the March/April observing season. Seven of the existing EHT sites have 345 GHz opacity less than 0.5 during observing months. Out of more than 40 candidate new locations analyzed, approximately half have 230 GHz opacity comparable to the existing EHT sites, and at least 17 of the candidate sites would be comparably good for 345 GHz observing. A group of new sites with favorable transmittance and geographic placement leads to greatly enhanced imaging and science on horizon scales.
Scale Normalized Image Pyramids With AutoFocus for Object Detection

We present an efficient foveal framework to perform object detection. A scale normalized image pyramid (SNIP) is generated that, like human vision, only attends to objects within a fixed size range at different scales. Such a restriction of objects’ size during training affords better learning of object-sensitive filters, and therefore, results in better accuracy. However, the use of an image pyramid increases the computational cost. Hence, we propose an efficient spatial sub-sampling scheme which only operates on fixed-size sub-regions likely to contain objects (as object locations are known during training). The resulting approach, referred to as Scale Normalized Image Pyramid with Efficient Resampling or SNIPER, yields up to 3× speed-up during training. Unfortunately, as object locations are unknown during inference, the entire image pyramid still needs processing. To this end, we adopt a coarse-to-fine approach, and predict the locations and extent of object-like regions which will be processed in successive scales of the image pyramid. Intuitively, it's akin to our active human-vision that first skims over the field-of-view to spot interesting regions for further processing and only recognizes objects at the right resolution. The resulting algorithm is referred to as AutoFocus and results in a 2.5-5× speed-up during inference when used with SNIP. Code: https://github.com/mahyarnajibi/SNIPER.
Fe b 20 21 η-meson leading-twist distribution amplitude within QCD sum rule approach and its application to the semi-leptonic decay D + s →

In this paper, we make a detailed discussion on the η-meson leading-twist light-cone distribution amplitude (LCDA) φ2;η(u, μ) by using the QCD sum rules approach under the background field theory. Taking both the non-perturbative condensates up to dimension-six and the next-to-leading order (NLO) QCD corrections to the perturbative part, its first three moments 〈ξn 2;η〉|μ0 with n = (2, 4, 6) can be determined, where the initial scale μ0 is set as the usual choice of 1 GeV. Numerically, we obtain 〈ξ2 2;η〉|μ0 = 0.204 −0.012, 〈ξ4 2;η〉|μ0 = 0.092 −0.007, and 〈ξ6 2;η〉|μ0 = 0.054 −0.005. Next, we calculate the Ds → η transition form factor (TFF) f+(q ) within the QCD light-cone sum rules approach up to NLO level. Its value at the large recoil region is f+(0) = 0.484 +0.039 −0.036. After extrapolating the TFF to the allowable physical region, we then obtain the total decay widthes and the branching fractions of the semi-leptonic decay D s → ηlνl, i.e. Γ(D s → ηeνe) = (31.197 −4.323)× 10 GeV, B(D+ s → ηeνe) = 2.389 −0.331 for D + s → ηeνe channel, and Γ(D s → ημνμ) = (30.849 −4.273) × 10 GeV, B(D+ s → ημνμ) = 2.362 −0.327 for D s → ημνμ channel respectively. Those values show good agreement with the recent BES-III measurements.
Sub‐Seasonal Forecasting With a Large Ensemble of Deep‐Learning Weather Prediction Models

We present an ensemble prediction system using a Deep Learning Weather Prediction (DLWP) model that recursively predicts six key atmospheric variables with six‐hour time resolution. This computationally efficient model uses convolutional neural networks (CNNs) on a cubed sphere grid to produce global forecasts. The trained model requires just three minutes on a single GPU to produce a 320‐member set of six‐week forecasts at 1.4° resolution. Ensemble spread is primarily produced by randomizing the CNN training process to create a set of 32 DLWP models with slightly different learned weights. Although our DLWP model does not forecast precipitation, it does forecast total column water vapor and gives a reasonable 4.5‐day deterministic forecast of Hurricane Irma. In addition to simulating mid‐latitude weather systems, it spontaneously generates tropical cyclones in a one‐year free‐running simulation. Averaged globally and over a two‐year test set, the ensemble mean RMSE retains skill relative to climatology beyond two‐weeks, with anomaly correlation coefficients remaining above 0.6 through six days. Our primary application is to subseasonal‐to‐seasonal (S2S) forecasting at lead times from two to six weeks. Current forecast systems have low skill in predicting one‐ or 2‐week‐average weather patterns at S2S time scales. The continuous ranked probability score (CRPS) and the ranked probability skill score (RPSS) show that the DLWP ensemble is only modestly inferior in performance to the European Center for Medium Range Weather Forecasts (ECMWF) S2S ensemble over land at lead times of 4 and 5–6 weeks. At shorter lead times, the ECMWF ensemble performs better than DLWP.
LIFT-CAM: Towards Better Explanations for Class Activation Mapping

Increasing demands for understanding the internal behaviors of convolutional neural networks (CNNs) have led to remarkable improvements in explanation methods. Particularly, several class activation mapping (CAM) based methods, which generate visual explanation maps by a linear combination of activation maps from CNNs, have been proposed. However, the majority of the methods lack a theoretical basis in how to assign their weighted linear coef-ﬁcients. In this paper, we revisit the intrinsic linearity of CAM w.r. t. the activation maps. Focusing on the linearity, we construct an explanation model as a linear function of binary variables which denote the existence of the corresponding activation maps. With this approach, the explanation model can be determined by the class of additive feature attribution methods which adopts SHAP values as a uniﬁed measure of feature importance. We then demonstrate the efﬁcacy of the SHAP values as the weight coefﬁcients for CAM. However, the exact SHAP values are incalculable. Hence, we introduce an efﬁcient approximation method, referred to as LIFT-CAM. On the basis of DeepLIFT, our proposed method can estimate the true SHAP values quickly and accurately. Furthermore, it achieves better performances than the other previous CAM-based methods in qualitative and quantitative aspects.
Top-pair production via gluon fusion in the Standard Model effective field theory

We compute the leading corrections to the differential cross section for top-pair production via gluon fusion due to dimension-six operators at leading order in QCD. The Standard Model fields are assumed to couple only weakly to the hypothetical new sector. A systematic approach then suggests treating single insertions of the operator class containing gluon field strength tensors on the same footing as expli\-citly loop suppressed contributions from four-fermion operators. This is in particular the case for the chromomagnetic operator $Q_{(uG)}$ and the purely bosonic operators $Q_{(G)}$ and $Q_{(\varphi G)}$. All leading order dimension-six contributions are consequently suppressed with a loop factor $1/16\pi^2$.
Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach

We propose a black-box reduction that turns a certain reinforcement learning algorithm with optimal regret in a (near-)stationary environment into another algorithm with optimal dynamic regret in a non-stationary environment, importantly without any prior knowledge on the degree of non-stationarity. By plugging different algorithms into our black-box, we provide a list of examples showing that our approach not only recovers recent results for (contextual) multi-armed bandits achieved by very specialized algorithms, but also significantly improves the state of the art for (generalized) linear bandits, episodic MDPs, and infinite-horizon MDPs in various ways. Specifically, in most cases our algorithm achieves the optimal dynamic regret $\widetilde{\mathcal{O}}(\min\{\sqrt{LT}, \Delta^{1/3}T^{2/3}\})$ where $T$ is the number of rounds and $L$ and $\Delta$ are the number and amount of changes of the world respectively, while previous works only obtain suboptimal bounds and/or require the knowledge of $L$ and $\Delta$.
The Hawaii Infrared Parallax Program. V. New T-dwarf Members and Candidate Members of Nearby Young Moving Groups

We present a search for new planetary-mass members of nearby young moving groups (YMGs) using astrometry for 694 T and Y dwarfs, including 447 objects with parallaxes, mostly produced by recent large parallax programs from UKIRT and Spitzer. Using the BANYAN Σ and LACEwING algorithms, we identify 30 new candidate YMG members, with spectral types of T0–T9 and distances of 10–43 pc. Some candidates have unusually red colors and/or faint absolute magnitudes compared to field dwarfs with similar spectral types, providing supporting evidence for their youth, including four early-T dwarfs. We establish one of these, the variable T1.5 dwarf 2MASS J21392676+0220226, as a new planetary-mass member ( M Jup) of the Carina-Near group (200 ± 50 Myr) based on its full six-dimensional kinematics, including a new parallax measurement from CFHT. The high-amplitude variability of this object is suggestive of a young age, given the coexistence of variability and youth seen in previously known YMG T dwarfs. Our four latest-type (T8–T9) YMG candidates, WISE J031624.35+430709.1, ULAS J130217.21+130851.2, WISEPC J225540.74–311841.8, and WISE J233226.49–432510.6, if confirmed, will be the first free-floating planets (≈2–6 M Jup) whose ages and luminosities are compatible with both hot-start and cold-start evolutionary models, and thus overlap with the properties of the directly imaged planet 51 Eri b. Several of our early/mid-T candidates have peculiar near-infrared spectra, indicative of heterogenous photospheres or unresolved binarity. Radial velocity measurements needed for final membership assessment for most of our candidates await upcoming 20–30 m class telescopes. In addition, we compile all 15 known T7–Y1 benchmarks and derive a homogeneous set of their effective temperatures, surface gravities, radii, and masses.
Derivation of a constitutive model for the rheology of jammed soft suspensions from particle dynamics

Considering the rheology of two-dimensional soft suspensions above the jamming density, we derive a tensorial constitutive model from the microscopic particle dynamics. Starting from the equation governing the N-particle distribution, we derive an evolution equation for the stress tensor. This evolution equation is not closed, as it involves the pair and three-particle correlation functions. To close this equation, we first employ the standard Kirkwood closure relation to express the three-particle correlation function in terms of the pair correlation function. Then we use a simple and physically motivated parametrization of the pair correlation function to obtain a closed evolution equation for the stress tensor. The latter is naturally expressed as separate evolution equations for the pressure and for the deviatoric part of the stress tensor. These evolution equations provide us with a non-linear tensorial constitutive model describing the rheological response of a jammed soft suspension to an arbitrary uniform deformation. One of the advantages of this microscopically-rooted description is that the coefficients appearing in the constitutive model are known in terms of packing fraction and microscopic parameters.
Nodal Sets of Steklov Eigenfunctions near the Boundary: Inner Radius Estimates

We show that Steklov eigenfunctions in a bounded Lipschitz domain have wavelength dense nodal sets near the boundary, in contrast to what can happen deep inside the domain. Conversely, in a 2D Lipschitz domain $\Omega $, we prove that any nodal domain of a Steklov eigenfunction contains a half-ball centered at $\partial \Omega $ of radius $c_{\Omega }/{\lambda }$.
WIYN Open Cluster Study. LXXXV. Li in NGC 2243: Implications for Stellar and Galactic Evolution

High-dispersion spectra in the Li λ6708 region have been obtained and analyzed in the old, metal-deficient cluster NGC 2243. From Hydra spectra for 29 astrometric and radial velocity members, we derive rotational velocities, as well as [Fe/H], [Ca/H], [Si/H], and [Ni/H] based on 17, 1, 1, and 3 lines, respectively. Using ROBOSPECT, an automatic equivalent width measurement program, we derive [Fe/H] = −0.54 ± 0.11 (MAD), for an internal precision for the cluster [Fe/H] below 0.03 dex. Given the more restricted line set, comparable values for [Ca/H], [Si/H], and [Ni/H] are −0.48 ± 0.19, −0.44 ± 0.11, and −0.61 ± 0.06, respectively. With E(B − V) = 0.055, appropriate isochrones imply (m − M) = 13.2 ± 0.1 and an age of 3.6 ± 0.2 Gyr. Using available VLT spectra and published Li abundances, we construct an Li sample of over 100 stars extending from the tip of the giant branch to 0.5 mag below the Li dip. The Li dip is well populated and, when combined with results for NGC 6819 and Hyades/Praesepe, implies a mass/metallicity slope of 0.4 M ⊙/dex for the high-mass edge of the Li dip. The A(Li) distribution among giants reflects the degree of Li variation among the turnoff stars above the Li dip, itself a function of stellar mass and metallicity and strongly anticorrelated with a v rot distribution that dramatically narrows with age. Potential implications of these patterns for the interpretation of Li among dwarf and giant field populations, especially selection biases tied to age and metallicity, are discussed.
Improved Algorithms for Efficient Active Learning Halfspaces with Massart and Tsybakov noise

We give a computationally-efficient PAC active learning algorithm for d-dimensional homogeneous halfspaces that can tolerate Massart noise [Massart and Nédélec, 2006] and Tsybakov noise [Tsybakov, 2004]. Specialized to the η-Massart noise setting, our algorithm achieves an information-theoretically near-optimal label complexity of Õ ( d (1−2η) polylog( 1 ǫ ) ) under a wide range of unlabeled data distributions (specifically, the family of “structured distributions” defined in Diakonikolas et al. [2020a]). Under the more challenging Tsybakov noise condition, we identify two subfamilies of noise conditions, under which our efficient algorithm provides label complexity guarantees strictly lower than passive learning algorithms.
Dynamic Structural Impact of the COVID-19 Outbreak on the Stock Market and the Exchange Rate: A Cross-country Analysis Among BRICS Nations

COVID-19 has impacted the economy of almost every country in the world. Of particular interest are the responses of the economic indicators of developing nations (such as BRICS) to the COVID-19 shock. As an extension to our earlier work on the dynamic associations of pandemic growth, exchange rate, and stock market indices in the context of India, we look at the same question with respect to the BRICS nations. We use structural variable autoregression (SVAR) to identify the dynamic underlying associations across the normalized growth measurements of the COVID-19 cumulative case, recovery, and death counts, and those of the exchange rate, and stock market indices, using data over 203 days (March 12 - September 30, 2020). Using impulse response analyses, the COVID-19 shock to the growth of exchange rate was seen to persist for around 10+ days, and that for stock exchange was seen to be around 15 days. The models capture the contemporaneous nature of these shocks and the subsequent responses, potentially guiding to inform policy decisions at a national level. Further, causal inference-based analyses would allow us to infer relationships that are stronger than mere associations.
Quantifying the relative contribution of free virus and cell-to-cell transmission routes to the propagation of hepatitis C virus infections in vitro using an agent-based model

Experiments have shown that hepatitis C virus (HCV) infections in vitro disseminate both distally via the release and diffusion of cell-free virus through the medium, and locally via direct, cell-to-cell transmission. To determine the relative contribution of each mode of infection to HCV dissemination, we developed an agent-based model (ABM) that explicitly incorporates both distal and local modes of infection. The ABM tracks the concentration of extracellular infectious virus in the supernatant and the number of intracellular HCV RNA segments within each infected cell over the course of simulated in vitro HCV infections. Experimental data for in vitro HCV infections conducted in the presence and absence of free-virus neutralizing antibodies was used to validate the ABM and constrain the value of its parameters. We found that direct, cell-to-cell infection accounts for 99% (84%$-$100%, 95% credible interval) of infection events, making it the dominant mode of HCV dissemination in vitro. Yet, when infection via the free-virus route is blocked, a 57% reduction in the number of infection events at 72 hpi is observed experimentally; a result consistent with that found by our ABM. Taken together, these findings suggest that while HCV spread via cell-free virus contributes little to the total number of infection events in vitro, it plays a critical role in enhancing cell-to-cell HCV dissemination by providing access to distant, uninfected areas, away from the already established large infection foci.
Powerful Jets from Radiatively Efficient Disks, a Decades-Old Unresolved Problem in High Energy Astrophysics

The discovery of 3C 273 in 1963, and the emergence of the Kerr solution shortly thereafter, precipitated the current era in astrophysics focused on using black holes to explain active galactic nuclei (AGN). But while partial success was achieved in separately explaining the bright nuclei of some AGN via thin disks, as well as powerful jets with thick disks, the combination of both powerful jets in an AGN with a bright nucleus, such as in 3C 273, remained elusive. Although numerical simulations have taken center stage in the last 25 years, they have struggled to produce the conditions that explain them. This is because radiatively efficient disks have proved a challenge to simulate. Radio quasars have thus been the least understood objects in high energy astrophysics. But recent simulations have begun to change this. We explore this milestone in light of scale-invariance and show that transitory jets, possibly related to the jets seen in these recent simulations, as some have proposed, cannot explain radio quasars. We then provide a road map for a resolution.
On the Robustness of Multi-View Rotation Averaging

Rotation averaging is a synchronization process on single or multiple rotation groups, and is a fundamental problem in many computer vision tasks such as multi-view structure from motion (SfM). Specifically, rotation averaging involves the recovery of an underlying pose-graph consistency from pairwise relative camera poses. Specifically, given pairwise motion in rotation groups, especially 3-dimensional rotation groups (\eg, $\mathbb{SO}(3)$), one is interested in recovering the original signal of multiple rotations with respect to a fixed frame. In this paper, we propose a robust framework to solve multiple rotation averaging problem, especially in the cases that a significant amount of noisy measurements are present. By introducing the $\epsilon$-cycle consistency term into the solver, we enable the robust initialization scheme to be implemented into the IRLS solver. Instead of conducting the costly edge removal, we implicitly constrain the negative effect of erroneous measurements by weight reducing, such that IRLS failures caused by poor initialization can be effectively avoided. Experiment results demonstrate that our proposed approach outperforms state of the arts on various benchmarks.
Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision

Using a model of the environment, reinforcement learning agents can plan their future moves and achieve superhuman performance in board games like Chess, Shogi, and Go, while remaining relatively sample-efficient. As demonstrated by the MuZero Algorithm, the environment model can even be learned dynamically, generalizing the agent to many more tasks while at the same time achieving state-of-the-art performance. Notably, MuZero uses internal state representations derived from real environment states for its predictions. In this paper, we bind the model's predicted internal state representation to the environment state via two additional terms: a reconstruction model loss and a simpler consistency loss, both of which work independently and unsupervised, acting as constraints to stabilize the learning process. Our experiments show that this new integration of reconstruction model loss and simpler consistency loss provide a significant performance increase in OpenAI Gym environments. Our modifications also enable self-supervised pretraining for MuZero, so the algorithm can learn about environment dynamics before a goal is made available.
Gendered impact of COVID-19 pandemic on research production: a cross-country analysis

The massive shock of the COVID-19 pandemic is already showing its negative effects on economies around the world, unprecedented in recent history. COVID-19 infections and containment measures have caused a general slowdown in research and new knowledge production. Because of the link between R amd D spending and economic growth, it is to be expected then that a slowdown in research activities will slow in turn the global recovery from the pandemic. Many recent studies also claim an uneven impact on scientific production across gender. In this paper, we investigate the phenomenon across countries, analysing preprint depositions in bioRxiv. Differently from other works, that compare the number of preprint depositions before and after the pandemic outbreak, we analyse the depositions trends across geographical areas, and contrast after-pandemic depositions with expected ones. Differently from common belief and initial evidence, in few countries female scientists increased their scientific output while males plunged. © 2021 18th International Conference on Scientometrics and Informetrics, ISSI 2021. All rights reserved.