Miracle-dz/UniFAR-FTUs huggingface dataset:
The GaussianSketch for Almost Relative Error Kernel Distance

We introduce two versions of a new sketch for approximately embedding the Gaussian kernel into Euclidean inner product space. These work by truncating infinite expansions of the Gaussian kernel, and carefully invoking the RecursiveTensorSketch [Ahle et al. SODA 2020]. After providing concentration and approximation properties of these sketches, we use them to approximate the kernel distance between points sets. These sketches yield almost $(1+\varepsilon)$-relative error, but with a small additive $\alpha$ term. In the first variants the dependence on $1/\alpha$ is poly-logarithmic, but has higher degree of polynomial dependence on the original dimension $d$. In the second variant, the dependence on $1/\alpha$ is still poly-logarithmic, but the dependence on $d$ is linear.
Deep Context Map: Agent Trajectory Prediction using Location-specific Latent Maps

In this paper, we propose a novel approach for agent motion prediction in cluttered environments. One of the main challenges in predicting agent motion is accounting for location and context-specific information. Our main contribution is the concept of learning context maps to improve the prediction task. Context maps are a set of location-specific latent maps that are trained alongside the predictor. Thus, the proposed maps are capable of capturing location context beyond visual context cues (e.g. usual average speeds and typical trajectories) or predefined map primitives (lanes and stop lines). We pose context map learning as a multi-task training problem and describe our map model and its incorporation into a state-of-the-art trajectory predictor. In extensive experiments, it is shown that use of maps can significantly improve predictor accuracy and be additionally boosted by providing even partial knowledge of map semantics.
A Variational Approach to a $L^1$-Minimization Problem Based on the Milman-Pettis Theorem

We develop a variational approach to the minimization problem of functionals of the type $\frac12\left\lVert \nabla \phi \right\rVert^2_2 + \beta \left\lVert \phi \right\rVert_1$ constrained by $\left\lVert \phi \right\rVert_2 = 1$ which is related to the characterization of cases satisfying the sharp Nash inequality. Employing theory of uniform convex spaces by Clarkson and the Milman-Pettis theorem we are able account for the non-reflexivity of $L^1$ and implement the direct method of calculus of variations. By deriving the Euler-Lagrange equation we verify that the minimizers are up to rearrangement compactly supported solutions to the inhomogeneous Helmholtz equation and we study their scaling behaviour in $\beta$.
Robust Automated Thalamic Nuclei Segmentation using a Multi-planar Cascaded Convolutional Neural Network

Purpose: To develop a fast, accurate, and robust convolutional neural network (CNN) based method for segmentation of thalamic nuclei. Methods: A cascaded multi-planar scheme with a modified residual U-Net architecture was used to segment thalamic nuclei on clinical datasets acquired using the white-matter-nulled Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequence. A single network was optimized for healthy controls and disease types (multiple sclerosis, essential tremor) and magnetic field strengths (3T and 7T). Another network was developed to use conventional MPRAGE data. Clinical utility was assessed by comparing a cohort of MS patients to healthy subjects. Results: Segmentation of each thalamus into 12 nuclei was achieved in under 4 minutes. For 7T WMn-MPRAGE, the proposed method outperformed current state-ofthe-art with statistically significant improvements in Dice ranging from 1.2% to 5.3% for MS and from 2.6% to 38.8% for ET patients. Comparable accuracy (Dice/VSI) was achieved between 7T and 3T data, attesting to the robustness of the method. For conventional MPRAGE, Dice of > 0.7 was achieved for larger nuclei and > 0.6 for the smaller nuclei. Atrophy of five thalamic nuclei and the whole thalamus was observed for MS patients compared to healthy control subjects, after controlling for intracranial volume and age (p<0.004). Conclusion: The proposed segmentation method is fast, accurate, and generalizes across disease types and field strengths and shows great potential for improving our understanding of thalamic nuclei involvement in neurological diseases and healthy aging.
Hydrodynamic Escape of Water Vapor Atmospheres near Very Active Stars

When exposed to the high-energy X-ray and ultraviolet radiation of a very active star, water vapor in the upper atmospheres of planets can be photodissociated and rapidly lost to space. In this paper, I study the chemical, thermal, and hydrodynamic processes in the upper atmospheres of terrestrial planets, concentrating on water-vapor-dominated atmospheres orbiting in the habitable zones of active stars. I consider different stellar activity levels and find very high levels of atmospheric escape in all cases, with the outflowing gas being dominated by atomic hydrogen and oxygen in both their neutral and ion forms. In the lower activity cases, I find that the accumulation of O2 and increases in the D/H ratios in the atmospheres due to mass fractionation are possible, but in the higher activity cases, no mass fractionation takes place. Connecting these results to stellar activity evolution tracks for solar-mass stars, I show that huge amounts of water vapor can be lost, and both the losses and the amount of O2 that can be accumulated in the atmosphere depend sensitively on the star’s initial rotation rate. For an Earth-mass planet in the habitable zone of a low-mass M dwarf, my results suggest that the accumulation of atmospheric O2 is unlikely unless water loss can take place after the star’s most active phase.
Utilizing Players' In-game Time Spending Records for Churn Prediction: Mining In-game Time Spending Regularity

In the free online game industry, churn prediction is an important research topic. Reducing the churn rate of a game significantly helps with the success of the game. Churn prediction helps a game operator identify possible churning players and keep them engaged in the game via appropriate operational strategies, marketing strategies, and/or incentives. Time spending related features are some of the widely used universal features for most churn prediction models. In this paper, we consider developing new universal features for churn predictions for long-term players based on player in-game time spending. In particular, we measure player time spending regularity using the notion of entropy and cross-entropy from information theory as the metric to characterize variance and change in the in-game time spent by a player, from data sets of six free online games of different types. We leverage information from players' in-game time spending regularity in the form of universal features for churn prediction. Experiments show that our developed features are better at predicting churners compared to baseline features. Thus, the experiment results imply that our proposed features could utilize the information extracted from players' in-game time spending more effectively than related baseline time spending features.
Diagnosing the Stellar Population and Tidal Structure of the Blanco 1 Star Cluster

We present the stellar population, using Gaia DR2 parallax, kinematics, and photometry, of the young (∼100 Myr), nearby (∼230 pc) open cluster, Blanco 1. A total of 644 member candidates are identified via the unsupervised machine learning method StarGO to find the clustering in the five-dimensional position and proper motion parameter (X, Y, Z, , μδ) space. Within the tidal radius of 10.0 ± 0.3 pc, there are 488 member candidates, 3 times more than those outside. A leading tail and a trailing tail, each of 50–60 pc in the Galactic plane, are found for the first time for this cluster, with stars further from the cluster center streaming away faster, manifest stellar stripping. Blanco 1 has a total detected mass of 285 ± 32 M⊙ with a mass function consistent with a slope of α = 1.35 ± 0.2 in the sense of , in the mass range of 0.25–2.51 M⊙, where N is the number of members and m is stellar mass. A minimum spanning tree (ΛMSR) analysis shows the cluster to be moderately mass segregated among the most massive members (≳1.4 M⊙), suggesting an early stage of dynamical disintegration.
Stochastic Lagrangian Dynamics of Vorticity. I. General Theory

Prior mathematical work of Constantin and Iyer (2008, 2011) has shown that incompressible Navier-Stokes solutions possess infinitely-many stochastic Lagrangian conservation laws for vorticity, backward in time, which generalize the invariants of Cauchy (1815) for smooth Euler solutions. We simplify this theory for the case of wall-bounded flows by appealing to the Kuz'min (1983)-Oseledets (1989) representation of Navier-Stokes dynamics, in terms of the vortex-momentum density associated to a continuous distribution of infinitesimal vortex rings. The Constantin-Iyer theory provides an exact representation for vorticity at any interior point as an average over stochastic vorticity contributions transported from the wall. We discuss relations of this Lagrangian formulation with the Eulerian theory of Lighthill (1963)-Morton (1984) for vorticity generation at solid walls, and also with a statistical result of Taylor (1932)-Huggins (1994), which connects dissipative drag with organized cross-stream motion of vorticity and which is closely analogous to the "Josephson-Anderson relation" for quantum superfluids. We elaborate a Monte Carlo numerical Lagrangian scheme to calculate the stochastic Cauchy invariants and their statistics, given the Eulerian space-time velocity field. The method is validated using an online database of a turbulent channel-flow simulation (Graham et al. 2016), where conservation of the mean Cauchy invariant is verified for two selected buffer-layer events corresponding to an "ejection" and a "sweep". The variances of the stochastic Cauchy invariants grow exponentially backward in time, however, confirming earlier observations of Lagrangian chaos in channel-flow turbulence.
Learning discriminative and robust time-frequency representations for environmental sound classification

Convolutional neural networks (CNN) are one of the best-performing neural network architectures for environmental sound classification (ESC). Recently, attention mechanisms have been used in CNN to capture the useful information from the audio signal for sound classification, especially for weakly labelled data where the timing information about the acoustic events is not available in the training data, apart from the availability of sound class labels. In these methods, however, the inherent time-frequency characteristics and variations are not explicitly exploited when obtaining the deep features. In this paper, we propose a new method, called time-frequency enhancement block (TFBlock), which temporal attention and frequency attention are employed to enhance the features from relevant frames and frequency bands. Compared with other attention mechanisms, in our method, parallel branches are constructed which allow the temporal and frequency features to be attended respectively in order to mitigate interference from the sections where no sound events happened in the acoustic environments. The experiments on three benchmark ESC datasets show that our method improves the classification performance and also exhibits robustness to noise.
Sepsis World Model: A MIMIC-based OpenAI Gym "World Model" Simulator for Sepsis Treatment

Sepsis is a life-threatening condition caused by the body's response to an infection. In order to treat patients with sepsis, physicians must control varying dosages of various antibiotics, fluids, and vasopressors based on a large number of variables in an emergency setting. In this project we employ a "world model" methodology to create a simulator that aims to predict the next state of a patient given a current state and treatment action. In doing so, we hope our simulator learns from a latent and less noisy representation of the EHR data. Using historical sepsis patient records from the MIMIC dataset, our method creates an OpenAI Gym simulator that leverages a Variational Auto-Encoder and a Mixture Density Network combined with a RNN (MDN-RNN) to model the trajectory of any sepsis patient in the hospital. To reduce the effects of noise, we sample from a generated distribution of next steps during simulation and have the option of introducing uncertainty into our simulator by controlling the "temperature" variable. It is worth noting that we do not have access to the ground truth for the best policy because we can only evaluate learned policies by real-world experimentation or expert feedback. Instead, we aim to study our simulator model's performance by evaluating the similarity between our environment's rollouts with the real EHR data and assessing its viability for learning a realistic policy for sepsis treatment using Deep Q-Learning.
Propensity rules and interference effects in laser-assisted photoionization of helium and neon

We investigate the angle-resolved photoelectron spectra from laser-assisted photoionization for helium and neon atoms using an ab initio method based on time-dependent surface flux and configuration interaction singles. We find that the shape of the distributions can be interpreted using a propensity rule, an intrinsic difference in the absorption and the emission processes, as well as interference effects between multiple paths to the final angular momentum state. The difference between absorption and emission is hidden in the first sideband in neon due to the multiple competing m channels, while in the second sideband a qualitative difference between absorption and emission is observed. In the m-resolved case, a clear difference is observed already in the first sideband, as exemplified in helium and m-resolved neon.
Estimation and Validation of Ratio-based Conditional Average Treatment Effects Using Observational Data

Abstract While sample sizes in randomized clinical trials are large enough to estimate the average treatment effect well, they are often insufficient for estimation of treatment-covariate interactions critical to studying data-driven precision medicine. Observational data from real world practice may play an important role in alleviating this problem. One common approach in trials is to predict the outcome of interest with separate regression models in each treatment arm, and estimate the treatment effect based on the contrast of the predictions. Unfortunately, this simple approach may induce spurious treatment-covariate interaction in observational studies when the regression model is misspecified. Motivated by the need of modeling the number of relapses in multiple sclerosis (MS) patients, where the ratio of relapse rates is a natural choice of the treatment effect, we propose to estimate the conditional average treatment effect (CATE) as the ratio of expected potential outcomes, and derive a doubly robust estimator of this CATE in a semiparametric model of treatment-covariate interactions. We also provide a validation procedure to check the quality of the estimator on an independent sample. We conduct simulations to demonstrate the finite sample performance of the proposed methods, and illustrate their advantages on real data by examining the treatment effect of dimethyl fumarate compared to teriflunomide in MS patients. Supplementary materials for this article are available online.
An Environmentally Stable and Lead‐Free Chalcogenide Perovskite

Organic–inorganic halide perovskites are intrinsically unstable when exposed to moisture and/or light. Additionally, the presence of lead in many perovskites raises toxicity concerns. Herein, a thin film of barium zirconium sulfide (BaZrS3), a lead‐free chalcogenide perovskite, is reported. Photoluminescence and X‐ray diffraction measurements show that BaZrS3 is far more stable than methylammonium lead iodide (MAPbI3) in moist environments. Moisture‐ and light‐induced degradations in BaZrS3 and MAPbI3 are compared by using simulations and calculations based on density functional theory. The simulations reveal drastically slower degradation in BaZrS3 due to two factors—weak interaction with water and very low rates of ion migration. BaZrS3 photodetecting devices with photoresponsivity of ≈46.5 mA W−1 are also reported. The devices retain ≈60% of their initial photoresponse after 4 weeks under ambient conditions. Similar MAPbI3 devices degrade rapidly and show a ≈95% decrease in photoresponsivity in just 4 days. The findings establish the superior stability of BaZrS3 and strengthen the case for its use in optoelectronics. New possibilities for thermoelectric energy conversion using these materials are also demonstrated.
Validity of mean-field theory in a dissipative critical system: Liouvillian gap, 
PT
-symmetric antigap, and permutational symmetry in the 
XYZ
 model

We study the all-to-all connected $XYZ$ (anisotropic-Heisenberg) spin model with local and collective dissipations, comparing the results of mean field theory with the solution of the Lindblad quantum evolution. Leveraging the permutational symmetry of the model [N. Shammah et al., Phys Rev. A 98, 063815 (2018)], we find exactly (up to numerical precision) the steady state up to $N=95$ spins. We characterize criticality, studying, as a function of the number of spins $N$, the spin structure factor, the magnetization, the Liouvillian gap and the Von Neumann entropy of the steady state. Exploiting the weak $\mathcal{PT}$-symmetry of the model, we efficiently calculate the Liouvillian gap, introducing the idea of an antigap. For small anisotropy, we find a paramagnetic-to-ferromagnetic phase transition in agreement with the mean-field theory. For large anisotropy, instead, we find a significant discrepancy from the scaling of the low-anisotropy ferromagnetic phase. We also study other more experimentally-accessible witnesses of the transition, which can be used for finite-size studies, namely the bimodality coefficient and the angular averaged susceptibility. In contrast to the bimodality coefficient, the angular averaged susceptibility fails to capture the onset of the transition, in striking difference with respect to lower-dimensional studies. We also analyze the competition between local dissipative processes (which disentangle the spin system) and collective dissipative ones (generating entanglement). The nature of the phase transition is almost unaffected by the presence of these terms. Our results mark a stark difference with the common intuition that an all-to-all connected system should fall onto the mean-field solution also for intermediate number of spins.
An Unsupervised Domain-Independent Framework for Automated Detection of Persuasion Tactics in Text

With the increasing growth of social media, people have started relying heavily on the information shared therein to form opinions and make decisions. While such a reliance is motivation for a variety of parties to promote information, it also makes people vulnerable to exploitation by slander, misinformation, terroristic and predatorial advances. In this work, we aim to understand and detect such attempts at persuasion. Existing works on detecting persuasion in text make use of lexical features for detecting persuasive tactics, without taking advantage of the possible structures inherent in the tactics used. We formulate the task as a multi-class classification problem and propose an unsupervised, domain-independent machine learning framework for detecting the type of persuasion used in text, which exploits the inherent sentence structure present in the different persuasion tactics. Our work shows promising results as compared to existing work.
Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection

Deep learning has achieved many successes in medical imaging, including lung nodule segmentation and lung cancer prediction on computed tomography (CT). Recently, multi-task networks have shown to both offer additional estimation capabilities, and, perhaps more importantly, increased performance over single-task networks on a "main/primary" task. However, balancing the optimization criteria of multi-task networks across different tasks is an area of active exploration. Here, we extend a previously proposed 3D attention-based network with four additional multi-task subnetworks for the detection of lung cancer and four auxiliary tasks (diagnosis of asthma, chronic bronchitis, chronic obstructive pulmonary disease, and emphysema). We introduce and evaluate a learning policy, Periodic Focusing Learning Policy (PFLP), that alternates the dominance of tasks throughout the training. To improve performance on the primary task, we propose an Internal-Transfer Weighting (ITW) strategy to suppress the loss functions on auxiliary tasks for the final stages of training. To evaluate this approach, we examined 3386 patients (single scan per patient) from the National Lung Screening Trial (NLST) and de-identified data from the Vanderbilt Lung Screening Program, with a 2517/277/592 (scans) split for training, validation, and testing. Baseline networks include a single-task strategy and a multi-task strategy without adaptive weights (PFLP/ITW), while primary experiments are multi-task trials with either PFLP or ITW or both. On the test set for lung cancer prediction, the baseline single-task network achieved prediction AUC of 0.8080 and multi-task baseline failed to converge (AUC 0.6720). However, applying PFLP helped multi-task network clarify and achieved test set lung cancer prediction AUC of 0.8402. Furthermore, our ITW technique boosted the PFLP enabled multi-task network and achieved an AUC of 0.8462 (McNemar test, p < 0.01). In conclusion, adaptive consideration of multi-task learning weights is important, and PFLP and ITW are promising strategies.
Deep Poisoning Functions: Towards Robust Privacy-safe Image Data Sharing

As deep networks are applied to an ever-expanding set of computer vision tasks, protecting general privacy in image data has become a critically important goal. This paper presents a new framework for privacy-preserving data sharing that is robust to adversarial attacks and overcomes the known issues existing in previous approaches. We introduce the concept of a Deep Poisoning Function (DPF), which is a module inserted into a pre-trained deep network designed to perform a specific vision task. The DPF is optimized to deliberately poison image data to prevent known adversarial attacks, while ensuring that the altered image data is functionally equivalent to the non-poisoned data for the original task. Given this equivalence, both poisoned and non-poisoned data can be used for further retraining or fine-tuning. Experimental results on image classification and face recognition tasks prove the efficacy of the proposed method.
Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator

Multi-agent reinforcement learning has been successfully applied to a number of challenging problems. Despite these empirical successes, theoretical understanding of different algorithms is lacking, primarily due to the curse of dimensionality caused by the exponential growth of the state-action space with the number of agents. We study a fundamental problem of multi-agent linear quadratic regulator in a setting where the agents are partially exchangeable. In this setting, we develop a hierarchical actor-critic algorithm, whose computational complexity is independent of the total number of agents, and prove its global linear convergence to the optimal policy. As linear quadratic regulators are often used to approximate general dynamic systems, this paper provided an important step towards better understanding of general hierarchical mean-field multi-agent reinforcement learning.