Miracle-dz/UniFAR-FTUs huggingface dataset:
Compositional generalization through meta sequence-to-sequence learning

People can learn a new concept and use it compositionally, understanding how to "blicket twice" after learning how to "blicket. " In contrast, powerful sequence-to-sequence (seq2seq) neural networks fail such tests of compositionality, especially when composing new concepts together with existing concepts. In this paper, I show how memory-augmented neural networks can be trained to generalize compositionally through meta seq2seq learning. In this approach, models train on a series of seq2seq problems to acquire the compositional skills needed to solve new seq2seq problems. Meta se2seq learning solves several of the SCAN tests for compositional learning and can learn to apply implicit rules to variables.
Use of Emergency Departments by Frail Elderly Patients: Temporal Patterns and Case Complexity

Emergency department (ED) care for frail elderly patients is associated with an increased use of resources due to their complex medical needs and frequently difficult psycho-social situation. To better target their needs with specially trained staff, it is vital to determine the times during which these particular patients present to the ED. Recent research was inconclusive regarding this question and the applied methods were limited to coarse time windows. Moreover, there is little research on time variation of frail ED patients' case complexity. This study examines differences in arrival rates for frail vs. non-frail patients in detail and compares case complexity in frail patients within vs. outside of regular GP working hours. Arrival times and case variables (admission rate, ED length of stay [LOS], triage level and comorbidities) were extracted from the EHR of an ED in an urban German teaching hospital. We employed Poisson time series regression to determine patterns in hourly arrival rates over the week. Frail elderly patients presented more likely to the ED during already high frequented hours, especially at midday and in the afternoon. Case complexity for frail patients was significantly higher compared to non-frail patients, but varied marginally in time only with respect to triage level and ED LOS. The results suggest that frailty-attuned emergency care should be available in EDs during the busiest hours. Based on EHR data, hospitals thus can tailor their staff needs.
Dynamic prediction of competing risk events using landmark sub-distribution hazard model with multiple longitudinal biomarkers

The cause-specific cumulative incidence function quantifies the subject-specific disease risk with competing risk outcome. With longitudinally collected biomarker data, it is of interest to dynamically update the predicted cumulative incidence function by incorporating the most recent biomarker as well as the cumulating longitudinal history. Motivated by a longitudinal cohort study of chronic kidney disease, we propose a framework for dynamic prediction of end stage renal disease using multivariate longitudinal biomarkers, accounting for the competing risk of death. The proposed framework extends the local estimation-based landmark survival modeling to competing risks data, and implies that a distinct sub-distribution hazard regression model is defined at each biomarker measurement time. The model parameters, prediction horizon, longitudinal history and at-risk population are allowed to vary over the landmark time. When the measurement times of biomarkers are irregularly spaced, the predictor variable may not be observed at the time of prediction. Local polynomial is used to estimate the model parameters without explicitly imputing the predictor or modeling its longitudinal trajectory. The proposed model leads to simple interpretation of the regression coefficients and closed-form calculation of the predicted cumulative incidence function. The estimation and prediction can be implemented through standard statistical software with tractable computation. We conducted simulations to evaluate the performance of the estimation procedure and predictive accuracy. The methodology is illustrated with data from the African American Study of Kidney Disease and Hypertension.
Dry, aligning, dilute, active matter: A synthetic and self-contained overview.

These notes are based on lectures given during the Summer School `Active Matter and Nonequilibrium Statistical Physics', held in Les Houches in September 2018. Dry, aligning, dilute, active matter (DADAM), with its many adjectives, refers to a corner of the multidimensional, fast-growing field of active matter studies. This corner, however, has both historical and theoretical importance for the entire field. These lectures notes first describe this particular status of DADAM. We then provide an overview of our current knowledge of DADAM in a synthetic and coherent manner. This constitutes the bulk of these notes. For convenience, we first describe the phenomenology of simple active particle models representing the basic DADAM classes, limiting ourselves to two spatial dimensions, where most work has been performed. Then we discuss the continuous hydrodynamic theories derived from these models using the Boltzmann-Ginzburg-Landau approach. We show in particular that they are largely faithful to the microscopic level, albeit qualitatively. In the conclusion, we come back to the nodal situation of DADAM within active matter studies and provide perspectives on how one can expand DADAM knowledge into various directions, approaching more realistic and more complex situations in a controlled way.
Government Expenditure and Economic Growth in the EU: Long-Run Tendencies and Short-Term Adjustment

This paper analyses both the long and the short-run relation between government expenditure and potential output in EU countries by means of pooled mean group estimation (Pesaran, Shin, and Smith (1999)). Â Results show that, over a sample comprising EU-15 countries over the 1970-2003 period, it cannot be rejected the hypothesis of a common long-term elasticity between cyclically-adjusted primary expenditure and potential output close to unity. However, the long-run elasticity decreased considerably over the decades and is significantly higher than unity in catching-up countries, in fast-ageing countries, in low-debt countries, and in countries with weak numerical rules for the control of government spending. Â The average speed of adjustment of government expenditure to its long-tem relation is 3 years, but there are significant differences across countries. Anglo-Saxon and Nordic countries exhibit in general a faster adjustment process, while adjustment in Southern European countries appears somehow slower.
Privacy-Preserving Deep Visual Recognition: An Adversarial Learning Framework and A New Dataset

This paper aims to boost privacy-preserving visual recognition, an increasingly demanded feature in smart camera applications, using deep learning. We formulate a unique adversarial training framework, that learns a degradation transform for the original video inputs, in order to explicitly optimize the trade-off between target task performance and the associated privacy budgets on the degraded video. We carefully analyze and benchmark three different optimization strategies to train the resulting model. Notably, the privacy budget, often defined and measured in task-driven contexts, cannot be reliably indicated using any single model performance, because a strong protection of privacy has to sustain against any possible model that tries to hack privacy information. In order to tackle this problem, we propose two strategies: model restarting and model ensemble, which can be easily plug-and-play into our training algorithms and further improve the performance. Extensive experiments have been carried out and analyzed. On the other hand, few public datasets are available with both utility and privacy labels provided, making the power of data-driven (supervised) learning not yet fully unleashed on this task. We first discuss an innovative heuristic of cross-dataset training and evaluation, that jointly utilizes two datasets with target task and privacy labels respectively, for adversarial training. To further alleviate this challenge, we have constructed a new dataset, termed PA-HMDB51, with both target task (action) and selected privacy attributes (gender, age, race, nudity, and relationship) labeled on a frame-wise basis. This first-of-its-kind video dataset further validates the effectiveness of our proposed framework, and opens up new opportunities for the research community.
Zeroth-Order Stochastic Block Coordinate Type Methods for Nonconvex Optimization

We study (constrained) nonconvex (composite) optimization problems where the decision variables vector can be split into blocks of variables. Random block projection is a popular technique to handle this kind of problem for its remarkable reduction of the computational cost from the projection. However, this powerful method has not been proposed for the situation that first-order information is prohibited and only zeroth-order information is available. In this paper, we propose to develop different classes of zeroth-order stochastic block coordinate type methods. Zeroth-order block coordinate descent (ZS-BCD) is proposed for solving unconstrained nonconvex optimization problem. For composite optimization, we establish the zeroth-order stochastic block mirror descent (ZS-BMD) and its associated two-phase method to achieve the complexity bound for finding $(\epsilon,\Lambda)$-solution. Furthermore, we also establish zeroth-order stochastic block coordinate conditional gradient (ZS-BCCG) method for nonconvex (composite) optimization. By implementing ZS-BCCG method, in each iteration, only (approximate) linear programming subproblem needs to be solved on a random block instead of a rather costly projection subproblem on the whole decision space, in contrast to the existing traditional stochastic approximation methods. In what follows, an approximate ZS-BCCG method and corresponding two-phase ZS-BCCG method are proposed. This is also the first time that a two-phase BCCG method has been developed to achieve the $(\epsilon, \Lambda)$-solution of nonconvex composite optimization problem. To the best of our knowledge, the proposed results in this paper are new in stochastic nonconvex (composite) optimization literature.
EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction

For offering proactive services (e.g., personalized exercise recommendation) to the students in computer supported intelligent education, one of the fundamental tasks is predicting student performance (e.g., scores) on future exercises, where it is necessary to track the change of each student's knowledge acquisition during her exercising activities. Unfortunately, to the best of our knowledge, existing approaches can only exploit the exercising records of students, and the problem of extracting rich information existed in the materials (e.g., knowledge concepts, exercise content) of exercises to achieve both more precise prediction of student performance and more interpretable analysis of knowledge acquisition remains underexplored. To this end, in this paper, we present a holistic study of student performance prediction. To directly achieve the primary goal of performance prediction, we first propose a general Exercise-Enhanced Recurrent Neural Network (EERNN) framework by exploring both student's exercising records and the text content of corresponding exercises. In EERNN, we simply summarize each student's state into an integrated vector and trace it with a recurrent neural network, where we design a bidirectional LSTM to learn the encoding of each exercise from its content. For making final predictions, we design two implementations on the basis of EERNN with different prediction strategies, i.e., EERNNM with Markov property and EERNNA with Attention mechanism. Then, to explicitly track student's knowledge acquisition on multiple knowledge concepts, we extend EERNN to an explainable Exercise-aware Knowledge Tracing (EKT) framework by incorporating the knowledge concept information, where the student's integrated state vector is now extended to a knowledge state matrix. In EKT, we further develop a memory network for quantifying how much each exercise can affect the mastery of students on multiple knowledge concepts during the exercising process. Finally, we conduct extensive experiments and evaluate both EERNN and EKT frameworks on a large-scale real-world data. The results in both general and cold-start scenarios clearly demonstrate the effectiveness of two frameworks in student performance prediction as well as the superior interpretability of EKT.
Venus Upper Clouds and the UV Absorber From MESSENGER/MASCS Observations

One of the most intriguing, long‐standing questions regarding Venus's atmosphere is the origin and distribution of the unknown UV absorber, responsible for the absorption band detected at the near‐UV and blue range of Venus's spectrum. In this work, we use data collected by Mercury Atmospheric and Surface Composition Spectrometer (MASCS) spectrograph on board the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) mission during its second Venus flyby in June 2007 to address this issue. Spectra range from 0.3 μm to 1.5 μm including some gaseous H2O and CO2 bands, as well as part of the SO2 absorption band and the core of the UV absorption. We used the NEMESIS radiative transfer code and retrieval suite to investigate the vertical distribution of particles in the equatorial atmosphere and to retrieve the imaginary refractive indices of the UV absorber, assumed to be well mixed with Venus's small mode 1 particles. The results show a homogeneous equatorial atmosphere, with cloud tops (height for unity optical depth) at 75 ± 2 km above surface. The UV absorption is found to be centered at 0.34 ± 0.03 μm with a full width at half maximum of 0.14 ± 0.01 μm. Our values are compared with previous candidates for the UV aerosol absorber, among which disulfur oxide (S2O) and dioxide disulfur (S2O2) provide the best agreement with our results.
Impact of axle overload, asphalt pavement thickness and subgrade modulus on load equivalency factor using modified ESALs equation

Abstract Road infrastructure is used by various types of vehicles among which heavy vehicles imposes the most critical loading, causing damage in pavement structure, which ultimately leads to an increased maintenance and rehabilitation costs. During the design of road pavements, each type of vehicle is converted into equivalent standard axle load (ESAL) to consider their impact on road structure. This research study was conducted to quantify the number of overloaded vehicles from the data collected on a critical national highway and to compute the impact of overloaded axle loads on road pavement in terms of ESALs. The variation of truck factor with asphalt layer thickness and subgrade modulus was also studied. The truck factor was calculated according to modified French Pavement Design Guide equation. It was found that the impact of axle configuration on the pavement is significant, i.e. truck factor for 2-axle vehicles was almost 3.33 times the 3-axle vehicles and 5.45 times the 6-axle semi-trailers. It was also observed that the truck factor reduced by almost 47% with a twofold increase in asphalt layer thickness. The subgrade modulus had a very minimal effect on the truck factor.
Toward a Full MHD Jet Model of Spinning Black Holes. I. Framework and a Split Monopole Example

In this paper, we construct a framework for investigating the magnetohydrodynamical jet structure of spinning black holes (BHs), where electromagnetic fields and fluid motion are governed by the Grad–Shafranov equation and the Bernoulli equation, respectively. Assuming steady and axisymmetric jet structure, we can self-consistently obtain electromagnetic fields, fluid energy density, and velocity within the jet, given proper plasma loading and boundary conditions. Specifically, we structure the two coupled governing equations as two eigenvalue problems, and develop full numerical techniques for solving them. As an example, we explicitly solve the governing equations for the split monopole magnetic field configuration and simplified plasma loading on the stagnation surface where the poloidal fluid velocity vanishes. As expected, we find the rotation of magnetic field lines is dragged down by fluid inertia, and the fluid as a whole does not contribute to energy extraction from the central BH, i.e., the magnetic Penrose process is not working. However, if we decompose the charged fluid as two oppositely charged components, we find the magnetic Penrose process does work for one of the two components when the plasma loading is low enough.
Spatially Resolved Water Emission from Gravitationally Lensed Dusty Star-forming Galaxies at z ∼ 3

Water (H2O), one of the most ubiquitous molecules in the universe, has bright millimeter-wave emission lines that are easily observed at high redshift with the current generation of instruments. The low-excitation transition of H2O, (νrest = 987.927 GHz), is known to trace the far-infrared (FIR) radiation field independent of the presence of active galactic nuclei (AGNs) over many orders of magnitude in FIR luminosity ( ). This indicates that this transition arises mainly due to star formation. In this paper, we present spatially (∼0.″5 corresponding to ∼1 kiloparsec) and spectrally resolved (∼100 kms−1) observations of in a sample of four strong gravitationally lensed high-redshift galaxies with the Atacama Large Millimeter/submillimeter Array. In addition to increasing the sample of luminous (>1012 L⊙) galaxies observed with H2O, this paper examines the relation on resolved scales for the first time at high redshift. We find that is correlated with on both global and resolved kiloparsec scales within the galaxy in starbursts and AGN with average . We find that the scatter in the observed relation does not obviously correlate with the effective temperature of the dust spectral energy distribution or the molecular gas surface density. This is a first step in developing as a resolved star formation rate calibrator.
Triple transitivity and non-free actions in dimension one

We show that if $G$ is either: (1) a group of homeomorphisms of the circle such that the action of $G$ on $S^1$ is minimal, proximal, non-topologically free and satisfies some mild assumption; (2) a group of automorphisms of a tree $T$ such that the action of $G$ on the boundary $\partial T$ is minimal and non-topologically free; then the following holds: every 3-transitive faithful action of $G$ on a set is conjugate to the action on an orbit in $S^1$ or $\partial T$. As a corollary, we obtain sharp upper bounds for the transitivity degree of these groups. These results produce new classes of infinite groups whose transitivity degree is known. In the Appendix we show that if a group satisfies a non-trivial mixed identity, then either it contains a normal subgroup isomorphic to a finitary alternating group, or it has finite transitivity degree.
Pairwise Fairness for Ranking and Regression

We present pairwise fairness metrics for ranking models and regression models that form analogues of statistical fairness notions such as equal opportunity, equal accuracy, and statistical parity. Our pairwise formulation supports both discrete protected groups, and continuous protected attributes. We show that the resulting training problems can be efficiently and effectively solved using existing constrained optimization and robust optimization techniques developed for fair classification. Experiments illustrate the broad applicability and trade-offs of these methods.
Conversion coefficients from kerma to ambient dose and personal dose for X-ray spectra

In radiation protection, the protection quantity for whole-body exposure is effective dose E. Effective dose cannot be measured and operational quantities have been introduced for dose measurement and calibration of dosimeters and survey instruments. To overcome some shortcomings of the presently used operational quantities, ICRU Report Committee 26 introduces two new quantities for whole body exposure, ambient dose H* for prospective dose assessment and personal dose Hp(α) for retrospective measurements with personal dosimeters. Dosimeters and survey instruments are calibrated in reference fields, realised with radioisotopes and with well-defined X-ray spectra. In this paper, the spectrum-averaged conversion coefficients from kerma in air Ka to the new quantities ambient dose and personal dose are calculated and compared with the published coefficients for the present operational quantities. Especially at low energies (Eph < 40 keV), the new quantities are significantly lower than the present ones, thus correcting a strong overestimate of effective dose.
On the Interpretation of Parker Solar Probe Turbulent Signals

In this Letter we propose a practical methodology to interpret future Parker Solar Probe (PSP) turbulent time signals even when Taylor’s hypothesis is not valid. By extending Kraichnan’s sweeping model used in hydrodynamics we derive the Eulerian spacetime correlation function in magnetohydrodynamic (MHD) turbulence. It is shown that in MHD, the temporal decorrelation of small-scale fluctuations arises from a combination of hydrodynamic sweeping induced by large-scale fluid velocity and by the Alfvénic propagation along the local magnetic field. The resulting temporal part of the spacetime correlation function is used to determine the field-perpendicular wavenumber range of the turbulent fluctuations that contribute to the power of a given frequency ω of the time signal measured in the spacecraft frame. Our analysis also shows that the shape of frequency power spectrum Psc(ω) of the time signal will follow the same power law of the reduced power spectrum in the plasma frame, where α is the spectral index. The proposed framework for the analysis of PSP time signals entirely relies on two simple dimensionless parameters that can be empirically obtained from PSP measurements, namely, (where V⊥ is the perpendicular velocity of PSP seen in the plasma frame) and the spectral index α.
In situ
 Raman gain between hyperfine ground states in a potassium magneto-optical trap

We study optical gain in a gas of cold 39K atoms. The gain is observed during operation of a conventional magneto-optical trap without the need for additional fields. Measurements of transmission spectra from a weak probe show that the gain is due to stimulated Raman scattering between hyperfine ground states. The experimental results are reproduced by a simplified six-level model, which also helps explain why such gain is not observed in similar experiments with rubidium or cesium.