Miracle-dz/UniFAR-FTUs huggingface dataset:
Elastic properties of self-folded two-dimensional nanomaterials: a theoretical model validated by molecular dynamics simulations

The trade-off between strength and ductility has plagued the design of macroscopic assemblies of two-dimensional materials for a long time. In order to break the strengthductility paradox, the design of self-folded two-dimensional nanomaterial (SF-2DNM) has been recently proposed with the inspiration from the folded nanostructures of natural silks. Such folding strategy is revealed to greatly enhance the ductility of overall assembly without much sacrifice of the excellent tensile strength of two-dimensional materials. However, the dependences of the elastic properties of SF-2DNMs on the material properties of building blocks and the geometries of folded structures have not been specifically clarified in previous studies. In this paper, we thus develop a theoretical model to describe the elastic properties of SF-2DNMs based on the shearlag analysis. The load transfer behaviors and failure modes of SF-2DNMs are demonstrated with this model. The Young’s modulus and tensile strength of SF-2DNMs are also predicted, further validated by molecular dynamics simulations. This model brings insights into the elastic deformation of SF-2DNMs under external tension. The structure-property relationship revealed by this model would provide useful guidelines for the rational design of SF-2DNMs in engineering applications.
ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images

Despite significant advancements of deep learning-based forgery detectors for distinguishing manipulated deepfake images, most detection approaches suffer from moderate to significant performance degradation with low-quality compressed deepfake images. Because of the limited information in low-quality images, detecting low-quality deepfake remains an important challenge. In this work, we apply frequency domain learning and optimal transport theory in knowledge distillation (KD) to specifically improve the detection of low-quality compressed deepfake images. We explore transfer learning capability in KD to enable a student network to learn discriminative features from low-quality images effectively. In particular, we propose the Attention-based Deepfake detection Distiller (ADD), which consists of two novel distillations: 1) frequency attention distillation that effectively retrieves the removed high-frequency components in the student network, and 2) multi-view attention distillation that creates multiple attention vectors by slicing the teacher’s and student’s tensors under different views to transfer the teacher tensor’s distribution to the student more efficiently. Our extensive experimental results demonstrate that our approach outperforms state-of-the-art baselines in detecting low-quality compressed deepfake images.
Manas: Mining Software Repositories to Assist AutoML

Today deep learning is widely used for building software. A software engineering problem with deep learning is that finding an appropriate convolutional neural network (CNN) model for the task can be a challenge for developers. Recent work on AutoML, more precisely neural architecture search (NAS), embodied by tools like Auto-Keras aims to solve this problem by essentially viewing it as a search problem where the starting point is a default CNN model, and mutation of this CNN model allows exploration of the space of CNN models to find a CNN model that will work best for the problem. These works have had significant success in producing high-accuracy CNN models. There are two problems, however. First, NAS can be very costly, often taking several hours to complete. Second, CNN models produced by NAS can be very complex that makes it harder to understand them and costlier to train them. We propose a novel approach for NAS, where instead of starting from a default CNN model, the initial model is selected from a repository of models extracted from GitHub. The intuition being that developers solving a similar problem may have developed a better starting point compared to the default model. We also analyze common layer patterns of CNN models in the wild to understand changes that the developers make to improve their models. Our approach uses commonly occurring changes as mutation operators in NAS. We have extended Auto-Keras to implement our approach. Our evaluation using 8 top voted problems from Kaggle for tasks including image classification and image regression shows that given the same search time, without loss of accuracy, Manas produces models with 42.9% to 99.6% fewer number of parameters than Auto-Keras' models. Benchmarked on GPU, Manas' models train 30.3% to 641.6% faster than Auto-Keras' models.
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach

Obtaining first-order regret bounds -- regret bounds scaling not as the worst-case but with some measure of the performance of the optimal policy on a given instance -- is a core question in sequential decision-making. While such bounds exist in many settings, they have proven elusive in reinforcement learning with large state spaces. In this work we address this gap, and show that it is possible to obtain regret scaling as $\widetilde{\mathcal{O}}(\sqrt{d^3 H^3 \cdot V_1^\star \cdot K} + d^{3.5}H^3\log K )$ in reinforcement learning with large state spaces, namely the linear MDP setting. Here $V_1^\star$ is the value of the optimal policy and $K$ is the number of episodes. We demonstrate that existing techniques based on least squares estimation are insufficient to obtain this result, and instead develop a novel robust self-normalized concentration bound based on the robust Catoni mean estimator, which may be of independent interest.
Multidimensional Diffusion MRI Methods With Confined Subdomains

Diffusion Magnetic Resonance Imaging (dMRI) is an imaging technique with exquisite sensitivity to the microstructural properties of heterogeneous media. The conventionally adopted acquisition schemes involving single pulsed field gradients encode the random motion of water molecules into the NMR signal, however typically conflating the effects of different sources contributing to the water motion. Time-varying magnetic field gradients have recently been considered for disentangling such effects during the data encoding phase, opening to the possibility of adding specificity to the recovered information about the medium’s microstructure. Such data is typically represented via a diffusion tensor distribution (DTD) model, thus assuming the existence of several non-exchanging compartments in each of which diffusion is unrestricted. In this work, we consider a model that takes confinement into account and possesses a diffusion time-dependence closer to that of restricted diffusion, to replace the free diffusion assumption in multidimensional diffusion MRI methods. We first demonstrate how the confinement tensor model captures the relevant signal modulations impressed by water diffusing in both free and closed spaces, for data simulated with a clinically feasible protocol involving time-varying magnetic field gradients. Then, we provide the basis for incorporating this model into two multidimensional dMRI methods, and attempt to recover a confinement tensor distribution (CTD) on a human brain dataset.
Estimating the non-dimensional energy of vortex rings by modelling their roll-up

Abstract The non-dimensional energy of starting vortex rings typically converges to values around 0.33 when they are created by a piston-cylinder or a bluff body translating at a constant speed. To explore the limits of the universality of this value and to analyse the variations that occur outside of those limits, we present an alternative approach to the slug-flow model to predict the non-dimensional energy of a vortex ring. Our approach is based on the self-similar vortex sheet roll-up described by Pullin (J. Fluid Mech., vol. 88, 1978, pp. 401–430). We derive the vorticity distribution for the vortex core resulting from a spiralling shear layer roll-up and compute the associated non-dimensional energy. To demonstrate the validity of our model for vortex rings generated through circular nozzles and in the wake of disks, we consider different velocity profiles of the vortex generator that follow a power law with a variable time exponent $m$. Higher values of $m$ indicate a more uniform vorticity distribution. For a constant velocity ($m=0$), our model yields a non-dimensional energy of ${{E}^{{*}}}=0.33$. For a constant acceleration ($m=1$), we find ${{E}^{{*}}}=0.19$. The limiting value $m \rightarrow \infty$ corresponds to a uniform vorticity distribution and leads to ${{E}^{{*}}}=0.16$, which is close to values found in the literature for Hill's spherical vortex. The radial diffusion of the vorticity within the vortex core results in the decrease of the non-dimensional energy. For a constant velocity, we obtain realistic vorticity distributions by radially diffusing the vorticity distribution of the Pullin spiral and predict a decrease of the non-dimensional energy from 0.33 to 0.28, in accordance with experimental results. Our proposed model offers a practical alternative to the existing slug flow model to predict the minimum non-dimensional energy of a vortex ring. The model is applicable to piston-generated and wake vortex rings and only requires the kinematics of the vortex generator as input.
Updated Photometry of the Yellow Supergiant Progenitor and Late-time Observations of the Type IIb Supernova SN 2016gkg

We present Hubble Space Telescope (HST) observations of the Type IIb supernova (SN) SN 2016gkg at 652, 1698, and 1795 days from explosion with the Advanced Camera for Surveys (ACS) and Wide Field Camera 3 (WFC3). Comparing to pre-explosion imaging from 2001 obtained with the Wide Field Planetary Camera 2, we demonstrate that SN 2016gkg is now fainter than its candidate counterpart in the latest WFC3 imaging, implying that the counterpart has disappeared and confirming that it was the SN progenitor star. We show the latest light curve and Keck spectroscopy of SN 2016gkg, which imply that SN 2016gkg is declining more slowly than the expected rate for 56Co decay during its nebular phase. We find that this emission is too luminous to be powered by other radioisotopes and infer that SN 2016gkg is entering a new phase in its evolution where it is powered primarily by interaction with circumstellar matter. Finally, we reanalyze the progenitor star spectral energy distribution and late-time limits in the context of binary evolution models. Including emission from a potential companion star, we find that all such predicted companion stars would be fainter than our limiting magnitudes.
A Pragmatic Account of the Weak Evidence Effect

Abstract Language is not only used to transmit neutral information; we often seek to persuade by arguing in favor of a particular view. Persuasion raises a number of challenges for classical accounts of belief updating, as information cannot be taken at face value. How should listeners account for a speaker’s “hidden agenda” when incorporating new information? Here, we extend recent probabilistic models of recursive social reasoning to allow for persuasive goals and show that our model provides a pragmatic account for why weakly favorable arguments may backfire, a phenomenon known as the weak evidence effect. Critically, this model predicts a systematic relationship between belief updates and expectations about the information source: weak evidence should only backfire when speakers are expected to act under persuasive goals and prefer the strongest evidence. We introduce a simple experimental paradigm called the Stick Contest to measure the extent to which the weak evidence effect depends on speaker expectations, and show that a pragmatic listener model accounts for the empirical data better than alternative models. Our findings suggest further avenues for rational models of social reasoning to illuminate classical decision-making phenomena.
Synthetic ECG Signal Generation Using Generative Neural Networks

Electrocardiogram (ECG) datasets tend to be highly imbalanced due to the scarcity of abnormal cases. Additionally, the use of real patients’ ECGs is highly regulated due to privacy issues. Therefore, there is always a need for more ECG data, especially for the training of automatic diagnosis machine learning models, which perform better when trained on a balanced dataset. We studied the synthetic ECG generation capability of 5 different models from the generative adversarial network (GAN) family and compared their performances, the focus being only on Normal cardiac cycles. Dynamic Time Warping (DTW), Fr´echet, and Euclidean distance functions were employed to quantitatively measure performance. Five different methods for evaluating generated beats were proposed and applied. We also proposed 3 new concepts (threshold, accepted beat and productivity rate) and employed them along with the aforementioned methods as a systematic way for comparison between models. The results show that all the tested models can, to an extent, successfully mass-generate acceptable heartbeats with high similarity in morphological features, and potentially all of them can be used to augment imbalanced datasets. However, visual inspections of generated beats favors BiLSTM-DC GAN and WGAN, as they produce statistically more acceptable beats. Also, with regards to productivity rate , the Classic GAN is superior with a 72% productivity rate. We also designed a simple experiment with the state-of-the-art classifier (ECGResNet34) to show empirically that the augmentation of the imbalanced dataset by synthetic ECG signals could improve the performance of classification significantly.
Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning

A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through natural language. Here we study how to design artificial agents that can interact naturally with humans using the simplification of a virtual environment. We show that imitation learning of human-human interactions in a simulated world, in conjunction with self-supervised learning, is sufficient to produce a multimodal interactive agent, which we call MIA, that successfully interacts with non-adversarial humans 75% of the time. We further identify architectural and algorithmic techniques that improve performance, such as hierarchical action selection. Altogether, our results demonstrate that imitation of multi-modal, real-time human behaviour may provide a straightforward and surprisingly effective means of imbuing agents with a rich behavioural prior from which agents might then be fine-tuned for specific purposes, thus laying a foundation for training capable agents for interactive robots or digital assistants. A video of MIA's behaviour may be found at https://youtu.be/ZFgRhviF7mY
Tell me why! Explanations support learning relational and causal structure

Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents. For humans, language--particularly in the form of explanations--plays a considerable role in overcoming this challenge. Here, we show that language can play a similar role for deep RL agents in complex environments. While agents typically struggle to acquire relational and causal knowledge, augmenting their experience by training them to predict language descriptions and explanations can overcome these limitations. We show that language can help agents learn challenging relational tasks, and examine which aspects of language contribute to its benefits. We then show that explanations can help agents to infer not only relational but also causal structure. Language can shape the way that agents to generalize out-of-distribution from ambiguous, causally-confounded training, and explanations even allow agents to learn to perform experimental interventions to identify causal relationships. Our results suggest that language description and explanation may be powerful tools for improving agent learning and generalization.
Private Robust Estimation by Stabilizing Convex Relaxations

We give the first polynomial time and sample (ε, δ)-differentially private (DP) algorithm to estimate the mean, covariance and higher moments in the presence of a constant fraction of adversarial outliers. Our algorithm succeeds for families of distributions that satisfy two well-studied properties in prior works on robust estimation: certifiable subgaussianity of directional moments and certifiable hypercontractivity of degree 2 polynomials. Our recovery guarantees hold in the “right affine-invariant norms”: Mahalanobis distance for mean, multiplicative spectral and relative Frobenius distance guarantees for covariance and injective norms for higher moments. Prior works obtained private robust algorithms for mean estimation of subgaussian distributions with bounded covariance. For covariance estimation, ours is the first efficient algorithm (even in the absence of outliers) that succeeds without any condition-number assumptions. Our algorithms arise from a new framework that provides a general blueprint for modifying convex relaxations for robust estimation to satisfy strong worst-case stability guarantees in the appropriate parameter norms whenever the algorithms produce witnesses of correctness in their run. We verify such guarantees for a modification of standard sum-of-squares (SoS) semidefinite programming relaxations for robust estimation. Our privacy guarantees are obtained by combining stability guarantees with a new “estimate dependent” noise injection mechanism in which noise scales with the eigenvalues of the estimated covariance. We believe this framework will be useful more generally in obtaining DP counterparts of robust estimators. Independently of our work, Ashtiani and Liaw (2021) also obtained a polynomial time and sample private robust estimation algorithm for Gaussian distributions.
The California Legacy Survey. III. On the Shoulders of (Some) Giants: The Relationship between Inner Small Planets and Outer Massive Planets

We use a high-precision radial velocity survey of FGKM stars to study the conditional occurrence of two classes of planets: close-in small planets (0.023–1 au, 2–30 M ⊕) and distant giant planets (0.23–10 au, 30–6000 M ⊕). We find that 41−13+15% of systems with a close-in, small planet also host an outer giant, compared to 17.6−1.9+2.4% for stars irrespective of small planet presence. This implies that small planet hosts may be enhanced in outer giant occurrences compared to all stars with 1. 7σ significance. Conversely, we estimate that 42−13+17% of cold giant hosts also host an inner small planet, compared to 27.6−4.8+5.8% of stars irrespective of cold giant presence. We also find that more massive and close-in giant planets are not associated with small inner planets. Specifically, our sample indicates that small planets are less likely to have outer giant companions more massive than approximately 120 M ⊕ and within 0.3–3 au, than to have less massive or more distant giant companions, with ∼2. 2σ confidence. This implies that massive gas giants within 0.3–3 au may suppress inner small planet formation. Additionally, we compare the host-star metallicity distributions for systems with only small planets and those with both small planets and cold giants. In agreement with previous studies, we find that stars in our survey that only host small planets have a metallicity distribution that is consistent with the broader solar-metallicity-median sample, while stars that host both small planets and gas giants are distinctly metal rich with ∼2. 3σ confidence.
Bogoliubov excitations of a polariton condensate in dynamical equilibrium with an incoherent reservoir

The classic Bogoliubov theory of weakly interacting Bose gases rests upon the assumption that nearly all the bosons condense into the lowest quantum state at suﬃciently low temperatures. Here we develop a generalized version of Bogoliubov theory for the case of a driven-dissipative exciton-polariton condensate with a large incoherent uncondensed component, or excitonic reservoir. We argue that such a reservoir can consist of both excitonic high-momentum polaritons and optically dark superpositions of excitons across diﬀerent optically active layers, such as multiple quantum wells in a microcavity. In particular, we predict interconversion between the dark and bright (light-coupled) excitonic states that can lead to a dynamical equilibrium between the condensate and reservoir populations. We show that the presence of the reservoir fundamentally modiﬁes both the energy and the amplitudes of the Bogoliubov quasiparticle excitations due to the non-Galilean-invariant nature of polaritons. Our theoretical ﬁndings are supported by our experiment, where we directly detect the Bogoliubov excitation branches of an optically trapped polariton condensate in the high-density regime. By analyzing the measured occupations of the excitation branches, we extract the Bogoliubov amplitudes across a range of momenta and show that they agree with our generalized theory.
Derivation of Stokes-plate-equations modeling fluid flow interaction with thin porous elastic layers

In this paper we investigate the interaction of fluid flow with a thin porous elastic layer. We consider two fluid-filled bulk domains which are separated by a thin periodically perforated layer consisting of a fluid and an elastic solid part. Thickness and periodicity of the layer are of order ϵ, where ϵ is small compared to the size of the bulk domains. The fluid flow is described by an instationary Stokes equation and the solid via linear elasticity. The main contribution of this paper is the rigorous homogenization of the porous structure in the layer and the reduction of the layer to an interface Σ in the limit using two-scale convergence. The effective model consists of the Stokes equation coupled to a time-dependent plate equation on the interface Σ including homogenized elasticity coefficients carrying information about the micro structure of the layer. In the zeroth-order approximation we obtain continuity of the velocities at the interface, where only a vertical movement occurs and the tangential components vanish. The tangential movement in the solid is of order ϵ and given as a Kirchhoff-Love displacement. Additionally, we derive higher-order correctors for the fluid in the thin layer.
Traversing within the Gaussian Typical Set: Differentiable Gaussianization Layers for Inverse Problems Augmented by Normalizing Flows

Generative networks such as normalizing ﬂows can serve as a learning-based prior to augment inverse problems to achieve high-quality results. However, the latent space vector may not remain a typical sample from the desired high-dimensional standard Gaussian distribution when traversing the latent space during an inversion. As a result, it can be challenging to attain a high-ﬁdelity so-lution, particularly in the presence of noise and inaccurate physics-based models. To address this issue, we propose to re-parameterize and Gaussianize the latent vector us-ing novel differentiable data-dependent layers wherein custom operators are deﬁned by solving optimization problems. These proposed layers enforce an inversion to ﬁnd a feasible solution within a Gaussian typical set of the latent space. We tested and validated our technique on an image deblur-ring task and eikonal tomography – a PDE-constrained inverse problem and achieved high-ﬁdelity results.