Miracle-dz/UniFAR-FTUs huggingface dataset:
High contrast imaging at the photon noise limit with self-calibrating WFS/C systems

High contrast imaging (HCI) systems rely on active wavefront control (WFC) to deliver deep raw contrast in the focal plane, and on calibration techniques to further enhance contrast by identifying planet light within the residual speckle halo. Both functions can be combined in an HCI system and we discuss a path toward designing HCI systems capable of calibrating residual starlight at the fundamental contrast limit imposed by photon noise. We highlight the value of deploying multiple high-efficiency wavefront sensors (WFSs) covering a wide spectral range and spanning multiple optical locations. We show how their combined information can be leveraged to simultaneously improve WFS sensitivity and residual starlight calibration, ideally making it impossible for an image plane speckle to hide from WFS telemetry. We demonstrate residual starlight calibration in the laboratory and on-sky, using both a coronagraphic setup, and a nulling spectro-interferometer. In both case, we show that bright starlight can calibrate residual starlight.
Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization

Developing robust vision-guided controllers for quadrupedal robots in complex environments with various obstacles, dynamical surroundings and uneven terrains is very challenging. While Reinforcement Learning (RL) provides a promising paradigm for agile locomotion skills with vision inputs in simulation, it is still very challenging to deploy the vision-guided RL policy in the real world. Our key insight is that the asynchronous multi-modal observations, caused by different latencies in different components of the real robot, create a large sim2real gap for a RL policy. In this paper, we propose Multi-Modal Delay Randomization (MMDR) to address this issue when training in simulation. Specifically, we randomize the selections for both the proprioceptive states and the visual observations in time during training, aiming to simulate the asynchronous inputs when deploying to the real robot. With this technique, we are able to train a RL policy for end-to-end locomotion control in simulation, which can be directly deployed on the real A1 quadruped robot running in the wild. We evaluate our method in different outdoor environments with complex terrain and obstacles. We show that the robot can smoothly maneuver at a high speed while avoiding the obstacles, achieving significant improvement over the baselines. Our project page with videos is at https://mehooz.github. io/mmdr-wild/.
Formulation and validation of a car-following model based on deep reinforcement learning

We propose and validate a novel car following model based on deep reinforcement learning. Our model is trained to maximize externally given reward functions for the free and car-following regimes rather than reproducing existing follower trajectories. The parameters of these reward functions such as desired speed, time gap, or accelerations resemble that of traditional models such as the Intelligent Driver Model (IDM) and allow for explicitly implementing different driving styles. Moreover, they partially lift the black-box nature of conventional neural network models. The model is trained on leading speed profiles governed by a truncated Ornstein-Uhlenbeck process reflecting a realistic leader’s kinematics. This allows for arbitrary driving situations and an infinite supply of training data. For various parameterizations of the reward functions, and for a wide variety of artificial and real leader data, the model turned out to be unconditionally string stable, comfortable, and crash-free. String stability has been tested with a platoon of five followers following an artificial and a real leading trajectory. A cross-comparison with the IDM calibrated to the goodness-of-fit of the relative gaps showed a higher reward compared to the traditional model and a better goodness-of-fit.
Gaussian Processes with Spectral Delta kernel for higher accurate Potential Energy surfaces for large molecules

The interpolation of high-dimensional potential energy surfaces (PESs) is commonly done with physically-inspired deep-neural network models. In this work, we illustrate that Gaussian Processes (GPs) are also capable of interpolating high-dimensional complex physical systems. The accuracy of GPs depends on the robustness of the kernel function, and a boost in the accuracy is achieved by linearly combining kernel functions. In this work, we proposed an alternative route by parametrizing the kernel function through Bochners' theorem. We interpolated the PES of various chemical systems achieving a global accuracy of<0.06 kcal/mol for Benzene, Malonaldehyde, Ethanol, and protonated Imidazole dimer using only 15 000 training points. Additionally, for Aspirin, we achieved a global error of 0.063 kcal/mol with 20 000 points. Given these results, we believe this kernel function is system-agnostic and could allow GPs to tackle a wider variety of high-dimensional physical systems.
Evaluation of dispersion interaction in glyoxal/silica organomineral system

It is proposed to use an organomineral additive based on glyoxal and saponite-containing waste in order to stabilize the road base. Preliminary studies have shown that soil modification with the organomineral additive with a quantitative ratio of components of glyoxal − 0.52 % and saponite-containing material − 17 % of sand mass increases its specific cohesion by 50 times. However, it is necessary to study the mechanism of interaction between the additive components and soil to optimize the composition and technology of additive introduction into the soil. This paper examines dispersion interaction in glyoxal/silica organomineral system. The Hamaker constant was chosen as a main parameter to evaluate dispersion interaction. This constant is determined based on the principle of measuring the contact angle of wetting with service fluids and calculating the dispersion component of material surface tension. The Hamaker constant in the glyoxal/silica organomineral system was 0.25·10−20 – 2.8·10−20 J at different glyoxal contents, while its maximum value was observed at 0.52 % glyoxal content. The results compared with literature and previously obtained data demonstrated good reproducibility.
An Energy Efficient Health Monitoring Approach with Wireless Body Area Networks

Wireless Body Area Networks (WBANs) comprise a network of sensors subcutaneously implanted or placed near the body surface and facilitate continuous monitoring of health parameters of a patient. Research endeavours involving WBAN are directed towards effective transmission of detected parameters to a Local Processing Unit (LPU, usually a mobile device) and analysis of the parameters at the LPU or a back-end cloud. An important concern in WBAN is the lightweight nature of WBAN nodes and the need to conserve their energy. This is especially true for subcutaneously implanted nodes that cannot be recharged or regularly replaced. Work in energy conservation is mostly aimed at optimising the routing of signals to minimise energy expended. In this article, a simple yet innovative approach to energy conservation and detection of alarming health status is proposed. Energy conservation is ensured through a two-tier approach wherein the first tier eliminates “uninteresting” health parameter readings at the site of a sensing node and prevents these from being transmitted across the WBAN to the LPU. The second tier of assessment includes a proposed anomaly detection model at the LPU that is capable of identifying anomalies from streaming health parameter readings and indicates an adverse medical condition. In addition to being able to handle streaming data, the model works within the resource-constrained environments of an LPU and eliminates the need of transmitting the data to a back-end cloud, ensuring further energy savings. The anomaly detection capability of the model is validated using data available from the critical care units of hospitals and is shown to be superior to other anomaly detection techniques.
Evaluating the robustness of targeted maximum likelihood estimators via realistic simulations in nutrition intervention trials

Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targeted maximum likelihood estimation. There are even more recent augmentations of these procedures that can increase robustness, by adding a layer of cross‐validation (cross‐validated targeted maximum likelihood estimation and double machine learning, as applied to substitution and estimating equation approaches, respectively). While these methods have been evaluated individually on simulated and experimental data sets, a comprehensive analysis of their performance across real data based simulations have yet to be conducted. In this work, we benchmark multiple widely used methods for estimation of the average treatment effect using ten different nutrition intervention studies data. A nonparametric regression method, undersmoothed highly adaptive lasso, is used to generate the simulated distribution which preserves important features from the observed data and reproduces a set of true target parameters. For each simulated data, we apply the methods above to estimate the average treatment effects as well as their standard errors and resulting confidence intervals. Based on the analytic results, a general recommendation is put forth for use of the cross‐validated variants of both substitution and estimating equation estimators. We conclude that the additional layer of cross‐validation helps in avoiding unintentional over‐fitting of nuisance parameter functionals and leads to more robust inferences.
Path integral and instantons for the process and phase transition rate of the RNAdS black hole

We propose a new approach to study the dynamical phase transition of RNAdS black holes on the underlying free energy landscape. By formulating a path integral framework, we can quantify the kinetic paths representing the history from the initial state to the end state, which provides us a visualized yet quantified picture about how the phase transition proceeds. Based on these paths, we derive the analytical formulas for the time evolution of the transition probability and provide a physical interpretation of the contribution to the probability from one “pseudomolecule” (“anti-pseudomolecule”), which is actually the phase transition rate from the small(large) to the large(small) black hole state. These numerical results show a good consistency with the underlying free energy landscape topography.
On the existence of canonical multi-phase Brakke flows

Abstract This paper establishes the global-in-time existence of a multi-phase mean curvature flow, evolving from an arbitrary closed rectifiable initial datum, which is a Brakke flow and a BV solution at the same time. In particular, we prove the validity of an explicit identity concerning the change of volume of the evolving grains, showing that their boundaries move according to the generalized mean curvature vector of the Brakke flow. As a consequence of the results recently established in [J. Fischer, S. Hensel, T. Laux and T. M. Simon, The local structure of the energy landscape in multiphase mean curvature flow: Weak-strong uniqueness and stability of evolutions, preprint 2020, https://arxiv.org/abs/2003.05478], under suitable assumptions on the initial datum, such additional property resolves the non-uniqueness issue of Brakke flows.
Geometry and causal flux in multi-loop Feynman diagrams

In this review, we discuss recent developments concerning efficient calculations of multi-loop multi-leg scattering amplitudes. Inspired by the remarkable properties of the Loop-Tree Duality (LTD), we explain how to reconstruct an integrand level representation of scattering amplitudes which only contains physical singularities. These so-called causal representations can be derived from connected binary partitions of Feynman diagrams, properly entangled according to specific rules. We will focus on the detection of flux orientations which are compatible with causality, describing the implementation of a quantum algorithm to identify such configurations.
Efficient Fourier representations of families of Gaussian processes

We introduce a class of algorithms for constructing Fourier representations of Gaussian processes in 1 dimension that are valid over ranges of hyperparameter values. The scaling and frequencies of the Fourier basis functions are evaluated numerically via generalized quadratures. The representations introduced allow for O ( N + m 3 ) inference via the non-uniform FFT where N is the number of data points and m is the number of basis functions. Numerical results are provided for Mat´ern kernels with ν ∈ [3 / 2 , 7 / 2] and ρ ∈ [0 . 1 , 0 . 5]. The algorithms of this paper generalize mathematically to higher dimensions, though they suﬀer from the standard curse of dimensionality.
EdinSaar@WMT21: North-Germanic Low-Resource Multilingual NMT

We describe the EdinSaar submission to the shared task of Multilingual Low-Resource Translation for North Germanic Languages at the Sixth Conference on Machine Translation (WMT2021). We submit multilingual translation models for translations to/from Icelandic (is), Norwegian-Bokmal (nb), and Swedish (sv). We employ various experimental approaches, including multilingual pre-training, back-translation, fine-tuning, and ensembling. In most translation directions, our models outperform other submitted systems.
The AUGUST Two-Sample Test: Powerful, Interpretable, and Fast

Two-sample testing is a fundamental problem in statistics, and many famous two-sample tests are designed to be fully non-parametric. These existing methods perform well with location and scale shifts but are less robust when faced with more exotic classes of alternatives, and rejections from these tests can be difficult to interpret. Here, we propose a new univariate non-parametric two-sample test, AUGUST, designed to improve on these aspects. AUGUST tests for inequality in distribution up to a predetermined resolution using symmetry statistics from binary expansion. The AUGUST statistic is exactly distribution-free and has a well-understood asymptotic distribution, permitting fast p-value computation. In empirical studies, we show that AUGUST has power comparable to that of the best existing methods in every context, as well as greater power in some circumstances. We illustrate the clear interpretability of AUGUST on NBA shooting data. ∗Benjamin Brown is a graduate student (Email: brownb1@live.unc.edu), Department of Statistics and Operations Research, University of North Carolina at Chapel Hill. †Kai Zhang is Associate Professor (Email: zhangk@email.unc.edu), Department of Statistics and Operations Research, University of North Carolina at Chapel Hill. Zhang’s research was partially supported by NSF DMS-1613112, NSF IIS-1633212, NSF DMS1916237. 1 ar X iv :2 10 9. 14 01 3v 2 [ st at . M E ] 8 O ct 2 02 1
Superconducting Fluctuations in Overdoped 
Bi2Sr2CaCu2O8+δ

Yu He*, 2, 3, 4 Su-Di Chen*, 3 Zi-Xiang Li*, Dan Zhao, 6 Dongjoon Song, Yoshiyuki Yoshida, Hiroshi Eisaki, Tao Wu, 6 Xian-Hui Chen, 6 Dong-Hui Lu, Christoph Meingast, Thomas P. Devereaux, 3 Robert J. Birgeneau, 4 Makoto Hashimoto†,8 Dung-Hai Lee†,2, 4 and Zhi-Xun Shen†1, 3 Department of Applied Physics, Stanford University, Stanford, CA 94305, USA Department of Physics, University of California at Berkeley, Berkeley, CA 94720, USA Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025, USA Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA∗ Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, China CAS Key Laboratory of Strongly-coupled Quantum Matter Physics, Department of Physics, University of Science and Technology of China, Hefei, Anhui 230026, China National Institute of Advanced Industrial Science and Technology, Tsukuba 305-8565, Japan Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA Institute for Quantum Materials and Technologies, Karlsruhe Institute of Technology, 76021 Karlsruhe, Germany Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
preCICE v2: A sustainable and user-friendly coupling library

preCICE is a free/open-source coupling library. It enables creating partitioned multi-physics simulations by gluing together separate software packages. This paper summarizes the development efforts in preCICE of the past five years. During this time span, we have turned the software from a working prototype -- sophisticated numerical coupling methods and scalability on ten thousands of compute cores -- to a sustainable and user-friendly software project with a steadily-growing community. Today, we know through forum discussions, conferences, workshops, and publications of more than 100 research groups using preCICE. We cover the fundamentals of the software alongside a performance and accuracy analysis of different data mapping methods. Afterwards, we describe ready-to-use integration with widely-used external simulation software packages, tests, and continuous integration from unit to system level, and community building measures, drawing an overview of the current preCICE ecosystem.
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification

We consider a variant of online binary classification where a learner sequentially assigns labels ($0$ or $1$) to items with unknown true class. If, but only if, the learner chooses label $1$ they immediately observe the true label of the item. The learner faces a trade-off between short-term classification accuracy and long-term information gain. This problem has previously been studied under the name of the `apple tasting' problem. We revisit this problem as a partial monitoring problem with side information, and focus on the case where item features are linked to true classes via a logistic regression model. Our principal contribution is a study of the performance of Thompson Sampling (TS) for this problem. Using recently developed information-theoretic tools, we show that TS achieves a Bayesian regret bound of an improved order to previous approaches. Further, we experimentally verify that efficient approximations to TS and Information Directed Sampling via P\'{o}lya-Gamma augmentation have superior empirical performance to existing methods.
Breaking the curse of dimensionality with Isolation Kernel

The curse of dimensionality has been studied in different aspects. However, breaking the curse has been elusive. We show for the first time that it is possible to break the curse using the recently introduced Isolation Kernel. We show that only Isolation Kernel performs consistently well in indexed search, spectral&density peaks clustering, SVM classification and t-SNE visualization in both low and high dimensions, compared with distance, Gaussian and linear kernels. This is also supported by our theoretical analyses that Isolation Kernel is the only kernel that has the provable ability to break the curse, compared with existing metric-based Lipschitz continuous kernels.
Adaptive-Resolution Gaussian Process Mapping for Efficient UAV-based Terrain Monitoring

—Unmanned aerial vehicles (UAVs) are rapidly gaining popularity in a variety of environmental monitoring tasks. A key requirement for autonomous operation is the ability to perform efﬁcient environmental mapping and path planning online, given their limited on-board resources constraining operation time and computational capacity. To address this, we present an adaptive-resolution approach for terrain mapping based on the Nd-tree structure and Gaussian Processes (GPs). Our approach enables retaining details in areas of interest using higher map resolutions while compressing information in uninteresting areas at coarser resolutions to achieve a compact map representation of the environment. A key aspect of our approach is an integral kernel encoding spatial correlation of 2D grid cells, which enables merging uninteresting grid cells in a theoretically sound way. Results show that our approach is more efﬁcient in terms of time and memory consumption without compromising on mapping quality. The resulting adaptive-resolution map accelerates the on-line adaptive path planning as well. Both performance enhancement in mapping and planning facilitate the efﬁciency of autonomous environmental monitoring with UAVs.
Live Visualization of Dynamic Software Cities with Heat Map Overlays

The 3D city metaphor in software visualization is a well-explored rendering method. Numerous tools use their custom variation to visualize offline-analyzed data. Heat map overlays are one of these variants. They introduce a separate information layer in addition to the software city’s own semantics. Results show that their usage facilitates program comprehension.In this paper, we present our heat map approach for the city metaphor visualization based on live trace analysis. In comparison to previous approaches, our implementation uses live dynamic analysis of a software system’s runtime behavior. At any time, users can toggle the heat map feature and choose which runtime-dependent metric the heat map should visualize. Our approach continuously and automatically renders both software cities and heat maps. It does not require a manual or semiautomatic generation of heat maps and seamlessly blends into the overall software visualization. We implemented this approach in our web-based tool ExplorViz, such that the heat map overlay is also available in our augmented reality environment. ExplorViz is developed as open source software and is continuously published via Docker images. A live demo of ExplorViz is publicly available.
Lorentz Symmetry and High-Energy Neutrino Astronomy

The search of the violation of Lorentz symmetry, or Lorentz violation (LV), is an active research field. The effects of LV are expected to be very small, and special systems are often used to search it. High-energy astrophysical neutrinos offer a unique system to search signatures of LV, due to the three factors: high neutrino energy, long propagation distance, and the presence of quantum mechanical interference. In this brief review, we introduce tests of LV and summarize existing searches of LV, using atmospheric and astrophysical neutrinos.