Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization

Identifying the atomic structure of organic–inorganic interfaces is challenging with current research tools. Interpreting the structure of complex molecular adsorbates from microscopy images can be difficult, and using atomistic simulations to find the most stable structures is limited to partial exploration of the potential energy surface due to the high-dimensional phase space. In this study, we present the recently developed Bayesian Optimization Structure Search (BOSS) method as an efficient solution for identifying the structure of non-planar adsorbates. We apply BOSS with density-functional theory simulations to detect the stable adsorbate structures of (1S)-camphor on the Cu(111) surface. We identify the optimal structure among eight unique types of stable adsorbates, in which camphor chemisorbs via oxygen (global minimum) or physisorbs via hydrocarbons to the Cu(111) surface. This study demonstrates that new cross-disciplinary tools, such as BOSS, facilitate the description of complex surface structures and their properties, and ultimately allow us to tune the functionality of advanced materials.
Tailoring inhomogeneous 
PT
-symmetric fiber-Bragg-grating spectra

The unique spectral behavior exhibited by a class of non-uniform Bragg periodic structures, namely chirped and apodized fiber Bragg gratings (FBGs) influenced by parity and time reversal ($\mathcal{PT}$) symmetry, is presented. The interplay between the $\mathcal{PT}$-symmetry and nonuniformities brings exceptional functionalities in the broken $\mathcal{PT}$-symmetric phase such as wavelength selective amplification and single-mode lasing for a wide range of variations in gain-loss. We observe that the device is no more passive and it undergoes a series of transitions from asymmetric reflection to unidirectional invisibility and multi-mode amplification as a consequence of variation in the imaginary part of the strength of modulation in different apodization profiles, namely Gaussian and raised cosine, at the given value of chirping. The chirping affords bandwidth control as well as control over the magnitude of the reflected (transmitted) light. Likewise, apodization offers additional functionality in the form of suppression of uncontrolled lasing behavior in the broken $\mathcal{PT}$-symmetric regime besides moderating the reflected signals outside the band edges of the spectra.
Are American options European after all?

We call a given American option representable if there exists a European claim which dominates the American payoff at any time and such that the values of the two options coincide in the continuation region of the American option. This concept has interesting implications from a probabilistic, analytic, financial, and numeric point of view. Relying on methods from Jourdain and Martini (2001, 2002), Chrsitensen (2014) and convex duality, we make a first step towards verifying representability of American options.
Object Detection on Single Monocular Images through Canonical Correlation Analysis

Without using extra 3-D data like points cloud or depth images for providing 3-D information, we retrieve the 3-D object information from single monocular images. The high-quality predicted depth images are recovered from single monocular images, and it is fed into the 2-D object proposal network with corresponding monocular images. Most existing deep learning frameworks with two-streams input data always fuse separate data by concatenating or adding, which views every part of a feature map can contribute equally to the whole task. However, when data are noisy, and too much information is redundant, these methods no longer produce predictions or classifications efficiently. In this report, we propose a two-dimensional CCA(canonical correlation analysis) framework to fuse monocular images and corresponding predicted depth images for basic computer vision tasks like image classification and object detection. Firstly, we implemented different structures with one-dimensional CCA and Alexnet to test the performance on the image classification task. And then, we applied one of these structures with 2D-CCA for object detection. During these experiments, we found that our proposed framework behaves better when taking predicted depth images as inputs with the model trained from ground truth depth.
Minimizing the Polarization Leakage of Geometric-phase Coronagraphs with Multiple Grating Pattern Combinations

The design of liquid-crystal diffractive phase plate coronagraphs for ground-based and space-based high-contrast imaging systems is limited by the trade-off between spectral bandwidth and polarization leakage. We demonstrate that by combining phase patterns with a polarization grating (PG) pattern directly followed by one or several separate PGs, we can suppress the polarization leakage terms by additional orders of magnitude by diffracting them out of the beam. Using two PGs composed of a single-layer liquid crystal structure in the lab, we demonstrate a leakage suppression of more than an order of magnitude over a bandwidth of 133 nm centered around 532 nm. At this center wavelength we measure a leakage suppression of three orders of magnitude. Furthermore, simulations indicate that a combination of two multi-layered liquid-crystal PGs can suppress leakage to <10−5 for 1–2.5 μm and <10−10 for 650–800 nm. We introduce multi-grating solutions with three or more gratings that can be designed to have no separation of the two circular polarization states, and offer even deeper suppression of polarization leakage. We present simulations of a triple-grating solution that has <10−10 leakage on the first Airy ring from 450 to 800 nm. We apply the double-grating concept to the Vector-vortex coronagraph of charge 4, and demonstrate in the lab that polarization leakage no longer limits the on-axis suppression for ground-based contrast levels. Lastly, we report on the successful installation and first-light results of a double-grating vector Apodizing Phase Plate pupil-plane coronagraph installed at the Large Binocular Telescope. We discuss the implications of these new coronagraph architectures for high-contrast imaging systems on the ground and in space.
Replacing Mobile Camera ISP with a Single Deep Learning Model

As the popularity of mobile photography is growing constantly, lots of efforts are being invested now into building complex hand-crafted camera ISP solutions. In this work, we demonstrate that even the most sophisticated ISP pipelines can be replaced with a single end-to-end deep learning model trained without any prior knowledge about the sensor and optics used in a particular device. For this, we present PyNET, a novel pyramidal CNN architecture designed for fine-grained image restoration that implicitly learns to perform all ISP steps such as image demosaicing, denoising, white balancing, color and contrast correction, demoireing, etc. The model is trained to convert RAW Bayer data obtained directly from mobile camera sensor into photos captured with a professional high-end DSLR camera, making the solution independent of any particular mobile ISP implementation. To validate the proposed approach on the real data, we collected a large-scale dataset consisting of 10 thousand full-resolution RAW-RGB image pairs captured in the wild with the Huawei P20 cameraphone (12.3 MP Sony Exmor IMX380 sensor) and Canon 5D Mark IV DSLR. The experiments demonstrate that the proposed solution can easily get to the level of the embedded P20’s ISP pipeline that, unlike our approach, is combining the data from two (RGB + B/W) camera sensors. The dataset, pretrained models and codes used in this paper are available on the project website: https://people.ee. ethz.ch/~ihnatova/pynet.html
Impact of collision models on the physical properties and the stability of lattice Boltzmann methods

The lattice Boltzmann method (LBM) is known to suffer from stability issues when the collision model relies on the BGK approximation, especially in the zero viscosity limit and for non-vanishing Mach numbers. To tackle this problem, two kinds of solutions were proposed in the literature. They consist in changing either the numerical discretization (finite-volume, finite-difference, spectral-element, etc.) of the discrete velocity Boltzmann equation (DVBE), or the collision model. In this work, the latter solution is investigated in detail. More precisely, we propose a comprehensive comparison of (static relaxation time based) collision models, in terms of stability, and with preliminary results on their accuracy, for the simulation of isothermal high-Reynolds number flows in the (weakly) compressible regime. It starts by investigating the possible impact of collision models on the macroscopic behaviour of stream-and-collide based D2Q9-LBMs, which clarifies the exact physical properties of collision models on LBMs. It is followed by extensive linear and numerical stability analyses, supplemented with an accuracy study based on the transport of vortical structures over long distances. In order to draw conclusions as generally as possible, the most common moment spaces (raw, central, Hermite, central Hermite and cumulant), as well as regularized approaches, are considered for the comparative studies. LBMs based on dynamic collision mechanisms (entropic collision, subgrid-scale models, explicit filtering, etc.) are also briefly discussed. This article is part of the theme issue ‘Fluid dynamics, soft matter and complex systems: recent results and new methods’.
Statistically significant observation of and cross-sections for a new nuclear reaction channel on 197Au with bound dineutron escape

A new nuclear reaction channel on 197Au with the neutron as a projectile and a bound dineutron (2n) as an ejectile in the output channel is considered based on available experimental observations. The dineutron is assumed to be formed as a particle satellite, separated from the volume but not from the potential well of the 196Au nucleus. The dineutron was identified by statistically significant radioactivity detection due to decay of Au nuclei. Cross-sections for the 197Au Au reaction are determined as and for [6.09–6.39] and [6.175–6.455] MeV energy ranges, correspondingly. Possible outcomes of dineutron detection near the surface of deformed nuclei are also raised and discussed.
Federated Learning of a Mixture of Global and Local Models

We propose a new optimization formulation for training federated learning models. The standard formulation has the form of an empirical risk minimization problem constructed to find a single global model trained from the private data stored across all participating devices. In contrast, our formulation seeks an explicit trade-off between this traditional global model and the local models, which can be learned by each device from its own private data without any communication. Further, we develop several efficient variants of SGD (with and without partial participation and with and without variance reduction) for solving the new formulation and prove communication complexity guarantees. Notably, our methods are similar but not identical to federated averaging / local SGD, thus shedding some light on the essence of the elusive method. In particular, our methods do not perform full averaging steps and instead merely take steps towards averaging. We argue for the benefits of this new paradigm for federated learning.
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees

Meta-learning can successfully acquire useful inductive biases from data, especially when a large number of meta-tasks are available. Yet, its generalization properties to unseen tasks are poorly understood. Particularly if the number of meta-tasks is small, this raises concerns about overfitting. We provide a theoretical analysis using the PAC-Bayesian framework and derive novel generalization bounds for meta-learning with unbounded loss functions and Bayesian base learners. Using these bounds, we develop a class of PAC-optimal meta-learning algorithms with performance guarantees and a principled meta-regularization. When instantiating our PAC-optimal hyper-posterior (PACOH) with Gaussian processes as base learners, the resulting method consistently outperforms several popular meta-learning methods, both in terms of predictive accuracy and the quality of uncertainty estimates.
Deep Hubble Space Telescope Imaging on the Extended Lyα Emission of a QSO at z = 2.19 with a Damped Lyman Alpha System as a Natural Coronagraph

Recent observations suggest that proximate damped Lyα (PDLA) systems can be used to study the host galaxies of quasi-stellar objects (QSOs), because the PDLAs can block the bright point-spread function from central QSOs. Using six orbits of narrowband imaging with Hubble Space Telescope (HST)/WFC3, we present the first high-resolution narrowband image of the Lyα emission in the PDLA trough of the QSO SDSS J115432.67–021537.9. We detect one major component and one minor component in the narrowband imaging. Combining the /WFC3 imaging with deep Magellan/MagE spectra, we measure that the Lyα flux = , which is among the luminous (≈2.7 ) Lyα emitters at 2.19. The -based star formation rate is ∼7 . These observational results favor that the star formation from the host galaxy could be the main mechanism powering the Lyα emission. This new method sheds light on the study of the kinematic structure and the spatial distribution of the extended Lyα emitting regions around the QSO host.
Deep Learning for Source Code Modeling and Generation

Deep Learning (DL) techniques for Natural Language Processing have been evolving remarkably fast. Recently, the DL advances in language modeling, machine translation, and paragraph understanding are so prominent that the potential of DL in Software Engineering cannot be overlooked, especially in the field of program learning. To facilitate further research and applications of DL in this field, we provide a comprehensive review to categorize and investigate existing DL methods for source code modeling and generation. To address the limitations of the traditional source code models, we formulate common program learning tasks under an encoder-decoder framework. After that, we introduce recent DL mechanisms suitable to solve such problems. Then, we present the state-of-the-art practices and discuss their challenges with some recommendations for practitioners and researchers as well.
Regularizing activations in neural networks via distribution matching with the Wasserstein metric

Regularization and normalization have become indispensable components in training deep neural networks, resulting in faster training and improved generalization performance. We propose the projected error function regularization loss (PER) that encourages activations to follow the standard normal distribution. PER randomly projects activations onto one-dimensional space and computes the regularization loss in the projected space. PER is similar to the Pseudo-Huber loss in the projected space, thus taking advantage of both $L^1$ and $L^2$ regularization losses. Besides, PER can capture the interaction between hidden units by projection vector drawn from a unit sphere. By doing so, PER minimizes the upper bound of the Wasserstein distance of order one between an empirical distribution of activations and the standard normal distribution. To the best of the authors' knowledge, this is the first work to regularize activations via distribution matching in the probability distribution space. We evaluate the proposed method on the image classification task and the word-level language modeling task.
Looking Enhances Listening: Recovering Missing Speech Using Images

Speech is understood better by using visual context; for this reason, there have been many attempts to use images to adapt automatic speech recognition (ASR) systems. Current work, however, has shown that visually adapted ASR models only use images as a regularization signal, while completely ignoring their semantic content. In this paper, we present a set of experiments where we show the utility of the visual modality under noisy conditions. Our results show that multimodal ASR models can recover words which are masked in the input acoustic signal, by grounding its transcriptions using the visual representations. We observe that integrating visual context can result in up to 35% relative improvement in masked word recovery. These results demonstrate that end-to-end multimodal ASR systems can become more robust to noise by leveraging the visual context.
Comprehensive and Efficient Data Labeling via Adaptive Model Scheduling

Labeling data comprehensively and efficiently is a widely needed but challenging task. With limited computing resources, given a data stream and a collection of deep-learning models, we propose to adaptively select and schedule a subset of these models to execute, aiming to maximize the value of the model output. Achieving this goal is nontrivial since a model’s output on any data item is content-dependent and hard to predict. In this paper, we present an Adaptive Model Scheduling framework, consisting of 1) a deep reinforcement learning-based approach to predict the value of unexecuted models by mining semantic relationship among diverse models, and 2) two heuristic algorithms to adaptively schedule models under deadline or deadline-memory constraints. The proposed framework does not require any prior knowledge of the data, which works as a powerful complement to existing model optimization technologies. We conduct extensive evaluations on 30 popular image labeling models to demonstrate the effectiveness of our design.
A joint reconstruction and lambda tomography regularization technique for energy-resolved x-ray imaging

We present new joint reconstruction and regularization techniques inspired by ideas in microlocal analysis and lambda tomography, for the simultaneous reconstruction of the attenuation coefficient and electron density from x-ray transmission (i.e., x-ray CT) and backscattered data (assumed to be primarily Compton scattered). To demonstrate our theory and reconstruction methods, we consider the ‘parallel line segment’ acquisition geometry of Webber J and Miller E (2020 Inverse Problems 36 025007), which is motivated by system architectures currently under development for airport security screening. We first present a novel microlocal analysis of the parallel line geometry which explains the nature of image artefacts when the attenuation coefficient and electron density are reconstructed separately. We next introduce a new joint reconstruction scheme for low effective Z (atomic number) imaging (Z < 20) characterized by a regularization strategy whose structure is derived from lambda tomography principles and motivated directly by the microlocal analytic results. Finally we show the effectiveness of our method in combating noise and image artefacts on simulated phantoms.
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization

Adaptivity is an important yet under-studied property in modern optimization theory. The gap between the state-of-the-art theory and the current practice is striking in that algorithms with desirable theoretical guarantees typically involve drastically different settings of hyperparameters, such as step-size schemes and batch sizes, in different regimes. Despite the appealing theoretical results, such divisive strategies provide little, if any, insight to practitioners to select algorithms that work broadly without tweaking the hyperparameters. In this work, blending the "geometrization" technique introduced by Lei & Jordan 2016 and the \texttt{SARAH} algorithm of Nguyen et al., 2017, we propose the Geometrized \texttt{SARAH} algorithm for non-convex finite-sum and stochastic optimization. Our algorithm is proved to achieve adaptivity to both the magnitude of the target accuracy and the Polyak-Łojasiewicz (PL) constant if present. In addition, it achieves the best-available convergence rate for non-PL objectives simultaneously while outperforming existing algorithms for PL objectives.
Convergence of a Stochastic Gradient Method with Momentum for Nonsmooth Nonconvex Optimization

Stochastic gradient methods with momentum are widely used in applications and at the core of optimization subroutines in many popular machine learning libraries. However, their sample complexities have never been obtained for problems that are non-convex and non-smooth. This paper establishes the convergence rate of a stochastic subgradient method with a momentum term of Polyak type for a broad class of non-smooth, non-convex, and constrained optimization problems. Our key innovation is the construction of a special Lyapunov function for which the proven complexity can be achieved without any tunning of the momentum parameter. For smooth problems, we extend the known complexity bound to the constrained case and demonstrate how the unconstrained case can be analyzed under weaker assumptions than the state-of-the-art. Numerical results confirm our theoretical developments.
Gushel--Mukai varieties: intermediate Jacobians

We describe intermediate Jacobians of Gushel-Mukai varieties $X$ of dimensions 3 or 5: if $A$ is the Lagrangian space associated with $X$, we prove that the intermediate Jacobian of $X$ is isomorphic to the Albanese variety of the canonical double covering of any of the two dual Eisenbud-Popescu-Walter surfaces associated with $A$. As an application, we describe the period maps for Gushel-Mukai threefolds and fivefolds. Comment: 48 pages. Latest addition to our series of articles on the geometry of Gushel-Mukai varieties; v2: minor stylistic improvements, results unchanged; v3: minor improvements; v4: final version, published in EPIGA
A simple model suggesting economically rational sample-size choice drives irreproducibility

Several systematic studies have suggested that a large fraction of published research is not reproducible. One probable reason for low reproducibility is insufficient sample size, resulting in low power and low positive predictive value. It has been suggested that insufficient sample-size choice is driven by a combination of scientific competition and ‘positive publication bias’. Here we formalize this intuition in a simple model, in which scientists choose economically rational sample sizes, balancing the cost of experimentation with income from publication. Specifically, assuming that a scientist’s income derives only from ‘positive’ findings (positive publication bias) and that individual samples cost a fixed amount, allows to leverage basic statistical formulas into an economic optimality prediction. We find that if effects have i) low base probability, ii) small effect size or iii) low grant income per publication, then the rational (economically optimal) sample size is small. Furthermore, for plausible distributions of these parameters we find a robust emergence of a bimodal distribution of obtained statistical power and low overall reproducibility rates, both matching empirical findings. Finally, we explore conditional equivalence testing as a means to align economic incentives with adequate sample sizes. Overall, the model describes a simple mechanism explaining both the prevalence and the persistence of small sample sizes, and is well suited for empirical validation. It proposes economic rationality, or economic pressures, as a principal driver of irreproducibility and suggests strategies to change this.