Miracle-dz/UniFAR-FTUs huggingface dataset:
Radioactive Planet Formation

Young stellar objects are observed to have large X-ray fluxes and are thought to produce commensurate luminosities in energetic particles (cosmic rays). This particle radiation, in turn, can synthesize short-lived radioactive nuclei through spallation. With a focus on 26Al, this paper estimates the expected abundances of radioactive nuclei produced by spallation during the epoch of planet formation. In this model, cosmic rays are accelerated near the inner truncation radii of circumstellar disks, r X ≈ 0.1 au, where intense magnetic activity takes place. For planets forming in this region, radioactive abundances can be enhanced over the values inferred for the early solar system (from meteoritic measurements) by factors of ∼10−20. These short-lived radioactive nuclei influence the process of planet formation and the properties of planets in several ways. The minimum size required for planetesimals to become fully molten decreases with increasing levels of radioactive enrichment, and such melting leads to loss of volatile components, including water. Planets produced with an enhanced radioactive inventory have significant internal luminosity, which can be comparable to that provided by the host star; this additional heating affects both atmospheric mass loss and chemical composition. Finally, the habitable zone of red dwarf stars is coincident with the magnetic reconnection region, so that planets forming at those locations will experience maximum exposure to particle radiation and subsequent depletion of volatiles.
Randomization-Based Test for Censored Outcomes: A New Look at the Logrank Test

Two-sample tests with censored outcomes are a classical topic in statistics with wide use even in cutting edge applications. There are at least two modes of inference used to justify two-sample tests. One is usual superpopulation inference assuming that units are independent and identically distributed (i.i.d.) samples from some superpopulation; the other is finite population inference that relies on the random assignments of units into different groups. When randomization is actually implemented, the latter has the advantage of avoiding distributional assumptions on the outcomes. In this paper, we focus on finite population inference for censored outcomes, which has been less explored in the literature. Moreover, we allow the censoring time to depend on treatment assignment, under which exact permutation inference is unachievable. We find that, surprisingly, the usual logrank test can also be justified by randomization. Specifically, under a Bernoulli randomized experiment with non-informative i.i. d. censoring, the logrank test is asymptotically valid for testing Fisher’s null hypothesis of no treatment effect on any unit. The asymptotic validity of the logrank test does not require any distributional assumption on the potential event times. We further extend the theory to the stratified logrank test, which is useful for randomized block designs and when censoring mechanisms vary across strata. In sum, the developed theory for the logrank test from finite population inference supplements its classical theory from usual superpopulation inference, and helps provide a broader justification for the logrank test.
E-PixelHop: An Enhanced PixelHop Method for Object Classification

Based on PixelHop and PixelHop++, which are recently developed using the successive subspace learning (SSL) framework, we propose an enhanced solution for object classification, called E-PixelHop, in this work. E-PixelHop consists of the following steps. First, to decouple the color channels for a color image, we apply principle component analysis and project RGB three color channels onto two principle subspaces which are processed separately for classification. Second, to address the importance of multi-scale features, we conduct pixel-level classification at each hop with various receptive fields. Third, to further improve pixel-level classification accuracy, we develop a soft-label smoothing (SLS) scheme to ensure prediction consistency. Forth, pixel-level decisions from each hop and from each color subspace are fused together for image-level decision. Fifth, to resolve confusing classes for further performance boosting, we formulate E-PixelHop as a two-stage pipeline. In the first stage, multi-class classification is performed to get a soft decision, where the top 2 classes with the highest probabilities are called confusing classes. Then, we conduct a binary classification in the second stage. The main contributions lie in Steps 1, 3 and 5. We use the classification of the CIFAR-10 dataset as an example to demonstrate the effectiveness of the above-mentioned key components of E-PixelHop.
Principles for Evaluation of AI/ML Model Performance and Robustness

The Department of Defense (DoD) has significantly increased its investment in the design, evaluation, and deployment of Artificial Intelligence and Machine Learning (AI/ML) capabilities to address national security needs. While there are numerous AI/ML successes in the academic and commercial sectors, many of these systems have also been shown to be brittle and nonrobust. In a complex and ever-changing national security environment, it is vital that the DoD establish a sound and methodical process to evaluate the performance and robustness of AI/ML models before these new capabilities are deployed to the field. This paper reviews the AI/ML development process, highlights common best practices for AI/ML model evaluation, and makes recommendations to DoD evaluators to ensure the deployment of robust AI/ML capabilities for national security needs.
Directed network Laplacians and random graph models

We consider spectral methods that uncover hidden structures in directed networks. We establish and exploit connections between node reordering via (a) minimizing an objective function and (b) maximizing the likelihood of a random graph model. We focus on two existing spectral approaches that build and analyse Laplacian-style matrices via the minimization of frustration and trophic incoherence. These algorithms aim to reveal directed periodic and linear hierarchies, respectively. We show that reordering nodes using the two algorithms, or mapping them onto a specified lattice, is associated with new classes of directed random graph models. Using this random graph setting, we are able to compare the two algorithms on a given network and quantify which structure is more likely to be present. We illustrate the approach on synthetic and real networks, and discuss practical implementation issues.
Can we observe weak localization of light in a hot atomic vapor?

N. Cherroret, ∗ M. Hemmerling, 3 G. Labeyrie, D. Delande, J.T. M. Walraven, and R. Kaiser Laboratoire Kastler Brossel, Sorbonne Université, CNRS, ENS-PSL Research University, Collège de France; 4 Place Jussieu, 75005 Paris, France Instituto de F́ısica de São Carlos, Universidade de São Paulo, 13560-970 São Carlos, SP, Brazil Université Côte d’Azur, CNRS, Institut de Physique de Nice, Valbonne F-06560, France Van der Waals-Zeeman Institute, Institute of Physics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
Novel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation

In this paper, we tackle the problem of novel visual category discovery, i.e., grouping unlabelled images from new classes into different semantic partitions by leveraging a labelled dataset that contains images from other different but relevant categories. This is a more realistic and challenging setting than conventional semi-supervised learning. We propose a two-branch learning framework for this problem, with one branch focusing on local part-level information and the other branch focusing on overall characteristics. To transfer knowledge from the labelled data to the unlabelled, we propose using dual ranking statistics on both branches to generate pseudo labels for training on the unlabelled data. We further introduce a mutual knowledge distillation method to allow information exchange and encourage agreement between the two branches for discovering new categories, allowing our model to enjoy the benefits of global and local features. We comprehensively evaluate our method on public benchmarks for generic object classification, as well as the more challenging datasets for fine-grained visual recognition, achieving state-of-the-art performance.
XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge

We present a new neural network, the XPDNet, for MRI reconstruction from periodically under-sampled multi-coil data. We inform the design of this network by taking best practices from MRI reconstruction and computer vision. We show that this network can achieve state-of-the-art reconstruction results, as shown by its ranking of second in the fastMRI 2020 challenge.
Insights into non-axisymmetric instabilities in three-dimensional rotating supernova models with neutrino and gravitational-wave signatures

We present a detailed analysis to clarify what determines the growth of the low-T/|W| instability in the context of rapidly rotating core collapse of massive stars. To this end, we perform three-dimensional core-collapse supernova (CCSN) simulations of a 27 M⊙ star including several updates in the general relativistic correction to gravity, the multi-energy treatment of heavy-lepton neutrinos, and the nuclear equation of state. Non-axisymmetric deformations are analyzed from the point of view of the time evolution of the pattern frequency and the corotation radius. The corotation radius is found to coincide with the convective layer in the proto neutron star (PNS). We propose a new mechanism to account for the growth of the low-T/|W| instability in the CCSN environment. Near the convective boundary where a small Brunt–Väisälä frequency is expected, Rossby waves propagating in the azimuthal direction at mid latitude induce non-axisymmetric unstable modes in both hemispheres. They merge with each other and finally become the spiral arm in the equatorial plane. We also investigate how the growth of the low-T/|W| instability impacts the neutrino and gravitational-wave signatures.
Robust Funnel Model Predictive Control for output tracking with prescribed performance

We propose a novel robust Model Predictive Control (MPC) scheme for nonlinear multi-input multi-output systems of relative degree one with stable internal dynamics. The proposed algorithm is a combination of funnel MPC, i.e., MPC with a particular stage cost, and the model-free adaptive funnel controller. The new robust funnel MPC scheme guarantees output tracking of reference signals within prescribed performance bounds -- even in the presence of unknown disturbances and a structural model-plant mismatch. We show initial and recursive feasibility of the proposed control scheme without imposing terminal conditions or any requirements on the prediction horizon. Moreover, we allow for model updates at runtime. To this end, we propose a proper initialization strategy, which ensures that recursive feasibility is preserved. Finally, we validate the performance of the proposed robust MPC scheme by simulations.
R2F: A Remote Retraining Framework for AIoT Processors With Computing Errors

Artificial Intelligence of Things (AIoT) processors fabricated with newer technology nodes suffer rising soft errors due to the shrinking transistor sizes and lower power supply. Soft errors on the AIoT processors particularly the deep learning accelerators (DLAs) with massive computing may cause substantial computing errors. These computing errors are difficult to be captured by the conventional training on general-purposed processors such as CPUs and GPUs in a server. Applying the offline trained neural network models to the edge accelerators with errors directly may lead to considerable prediction accuracy loss. To address the problem, we propose a remote retraining framework (R2F) for remote AIoT processors with computing errors. It takes the remote AIoT processor with soft errors in the training loop such that the on-site computing errors can be learned with the application data on the server and the retrained models can be resilient to the soft errors. Meanwhile, we propose an optimized partial triple modular redundancy (TMR) strategy to enhance the retraining. According to our experiments, R2F enables elastic design tradeoffs between the model accuracy and the performance penalty. The top-5 model accuracy can be improved by 1.93%–13.73% with 0%–200% performance penalty at high fault error rate. In addition, we notice that the retraining requires massive data transmission and even dominates the training time and propose a sparse increment compression approach for the data transmission optimization, which reduces the retraining time by 38%–88% on average with negligible accuracy loss over straightforward remote retraining.
Deep Learning Based Auction-Driven Beamforming for Wireless Information and Power Transfer

In this paper, we design a deep learning based resource allocation framework, in the form of an auction, for simultaneous information and power transfer from a hybrid access point (AP) to information devices and energy harvesting devices, respectively. Using Myerson’s lemma and the concept of virtual welfare maximization, we develop an optimal dominant-strategy incentive-compatible mechanism for the AP to maximize its expected revenue, based on the devices’ bid profiles, valuation distributions, demand profiles, and channel state information. In so doing, we formulate the revenue maximization problem, which is a mixed-integer non-linear program, and propose an efficient Branch-and-Bound (BnB) algorithm to solve the problem using semidefinite relaxation technique in each branch. Since the problem has exponential time complexity, using BnB algorithms can be impractical for real-time applications. To circumvent this, a deep neural network (DNN) is proposed, and trained to predict the optimal mechanism for beamforming the data and the energy towards the information and energy devices, respectively. We use the BnB algorithm to solve the problem offline and populate the training dataset. The proposed DNN architecture is indeed a multi-layer perceptron, which is trained well to map the heterogeneous input to the desired output with high accuracy. Furthermore, we propose a heuristic iterative solution whose accuracy performance is comparable to that of the DNN-based solution. The heuristic solution has polynomial time complexity whereas the DNN-based solution has linear time complexity.
Global-in-Time $L^p−L^q$ Estimates for Solutions of the Kramers-Fokker-Planck Equation

In this work, we prove an optimal global-in-time Lp−Lq estimate for solutions to the Kramers-Fokker-Planck equation with short range potential in dimension three. Our result shows that the decay rate as t → +∞ is the same as the heat equation in x-variables and the divergence rate as t → 0+ is related to the sub-ellipticity with loss of 1/3 derivatives of the Kramers-Fokker-Planck operator.
SiMPLeR: A Series-Elastic Manipulator with Passive Variable Stiffness for Legged Robots

We propose a mechanically simple and cheap design for a series elastic actuator with controllable stiffness. Such characteristics are necessary for animals for running, jumping, throwing, and manipulation, yet in robots, variable stiffness actuators are either complicated or mimicked at low bandwidth through feedback controllers. A robust and simple design is needed to build reliable and cheap robots with animal capabilities. The key insight of our design is attaching torsional springs to timing belts to create a variable stiffness linear spring. In an antagonistic pair, varying the distance between motor and joint then varies the actuator stiffness. We build a prototype of our proposed actuator, show the theoretical behavior matches the experimental characterization, and demonstrate an application to robotic one-legged hopping.
A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution

Recently, most of state-of-the-art single image super-resolution (SISR) methods have attained impressive performance by using deep convolutional neural networks (DCNNs). The existing SR methods have limited performance due to a fixed degradation settings, i.e. usually a bicubic downscaling of low-resolution (LR) image. However, in real-world settings, the LR degradation process is unknown which can be bicubic LR, bilinear LR, nearest-neighbor LR, or real LR. Therefore, most SR methods are ineffective and inefficient in handling more than one degradation settings within a single network. To handle the multiple degradation, i.e. refers to multi-domain image super-resolution, we propose a deep Super-Resolution Residual StarGAN (SR2*GAN), a novel and scalable approach that super-resolves the LR images for the multiple LR domains using only a single model. The proposed scheme is trained in a StarGAN like network topology with a single generator and discriminator networks. We demonstrate the effectiveness of our proposed approach in quantitative and qualitative experiments compared to other state-of-the-art methods.
Growth of aggregates with liquid-like ice shells in protoplanetary discs

During the first stages of planet formation, the collision growth of dust aggregates in protoplanetary discs (PPDs) is interrupted at the bouncing barrier. Dust aggregates coated by different species of ice turn out to be helpful to shift the bouncing barrier towards larger sizes due to their enhanced sticking properties in some cases. A rarely noticed fact is that H2O ice and H2O–CH3OH–NH3 ice behave liquid-like when UV irradiated within a PPD-similar environment. Dust aggregates coated by these ice species might be damped in collisions due to the liquid-like ice shell, which would effectively result in an increase of the sticking velocity. In this work, collisions of dust aggregates covered by the liquid-like H2O–CH3OH–NH3 ice shell are considered numerically. The coefficient of restitution and the sticking velocity are calculated for different thicknesses of the ice shell. The simulation predicts that an ice-shell thickness of few microns would be sufficient to allow the growth of cm-sized clusters in the PPD.
High Performance N-Doped Mesoporous Carbon Decorated TiO2 Nanofibers as Anode Materials for Lithium-Ion Batteries

N-doped mesoporous carbon decorated TiO2 nanofibers were synthesized for the first time by a facile electrospinning process combined with subsequent heat treatment and investigated as an anode material for lithium-ion battery applications. The N-doped mesoporous carbon decorated TiO2 nanofibers had continuous one-dimensional (1-D) geometry with a smooth surface and an average thickness of ∼250 nm. The nanofibers comprised both interconnected polycrystalline TiO2 and numerous mesopores in N-doped carbon. After 100 cycles at current density of 33 mA/g, the N-doped mesoporous carbon decorated TiO2 nanofibers still exhibited a high capacity of 264 mAh/g. This superior electrochemical performance is attributed to small TiO2 crystallites, N-doped mesoporous carbon, favorable 1-D nanostructures, and smooth accommodation of the strain occurring during the charge–discharge process.
Time-Aware Ancient Chinese Text Translation and Inference

In this paper, we aim to address the challenges surrounding the translation of ancient Chinese text: (1) The linguistic gap due to the difference in eras results in translations that are poor in quality, and (2) most translations are missing the contextual information that is often very crucial to understanding the text. To this end, we improve upon past translation techniques by proposing the following: We reframe the task as a multi-label prediction task where the model predicts both the translation and its particular era. We observe that this helps to bridge the linguistic gap as chronological context is also used as auxiliary information. We validate our framework on a parallel corpus annotated with chronology information and show experimentally its efficacy in producing quality translation outputs. We release both the code and the data for future research.
Probabilistic semi-nonnegative matrix factorization: a Skellam-based framework

We present a new probabilistic model to address semi-nonnegative matrix factorization (SNMF), called Skellam-SNMF. It is a hierarchical generative model consisting of prior components, Skellam-distributed hidden variables and observed data. Two inference algorithms are derived: Expectation-Maximization (EM) algorithm for maximum \emph{a posteriori} estimation and Variational Bayes EM (VBEM) for full Bayesian inference, including the estimation of parameters prior distribution. From this Skellam-based model, we also introduce a new divergence $\mathcal{D}$ between a real-valued target data $x$ and two nonnegative parameters $\lambda_{0}$ and $\lambda_{1}$ such that $\mathcal{D}\left(x\mid\lambda_{0},\lambda_{1}\right)=0\Leftrightarrow x=\lambda_{0}-\lambda_{1}$, which is a generalization of the Kullback-Leibler (KL) divergence. Finally, we conduct experimental studies on those new algorithms in order to understand their behavior and prove that they can outperform the classic SNMF approach on real data in a task of automatic clustering.
Edge-aware Bidirectional Diffusion for Dense Depth Estimation from Light Fields

We present an algorithm to estimate fast and accurate depth maps from light fields via a sparse set of depth edges and gradients. Our proposed approach is based around the idea that true depth edges are more sensitive than texture edges to local constraints, and so they can be reliably disambiguated through a bidirectional diffusion process. First, we use epipolar-plane images to estimate sub-pixel disparity at a sparse set of pixels. To find sparse points efficiently, we propose an entropy-based refinement approach to a line estimate from a limited set of oriented filter banks. Next, to estimate the diffusion direction away from sparse points, we optimize constraints at these points via our bidirectional diffusion method. This resolves the ambiguity of which surface the edge belongs to and reliably separates depth from texture edges, allowing us to diffuse the sparse set in a depth-edge and occlusion-aware manner to obtain accurate dense depth maps.
Tidal Evolution of the Earth–Moon System with a High Initial Obliquity

A giant-impact origin for the Moon is generally accepted, but many aspects of lunar formation remain poorly understood and debated. Ćuk et al. proposed that an impact that left the Earth–Moon system with high obliquity and angular momentum could explain the Moon’s orbital inclination and isotopic similarity to Earth. In this scenario, instability during the Laplace Plane transition, when the Moon’s orbit transitions from the gravitational influence of Earth’s figure to that of the Sun, would both lower the system’s angular momentum to its present-day value and generate the Moon’s orbital inclination. Recently, Tian & Wisdom discovered new dynamical constraints on the Laplace Plane transition and concluded that the Earth–Moon system could not have evolved from an initial state with high obliquity. Here we demonstrate that the Earth–Moon system with an initially high obliquity can evolve into the present state, and we identify a spin–orbit secular resonance as a key dynamical mechanism in the later stages of the Laplace Plane transition. Some of the simulations by Tian & Wisdom did not encounter this late secular resonance, as their model suppressed obliquity tides and the resulting inclination damping. Our results demonstrate that a giant impact that left Earth with high angular momentum and high obliquity (θ > 61°) is a promising scenario for explaining many properties of the Earth–Moon system, including its angular momentum and obliquity, the geochemistry of Earth and the Moon, and the lunar inclination.