Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse Depth Completion with Semantic Mesh Deformation Optimization

Sparse depth measurements are widely available in many applications such as augmented reality, visual inertial odometry and robots equipped with low cost depth sensors. Although such sparse depth samples work well for certain applications like motion tracking, a complete depth map is usually preferred for broader applications, such as 3D object recognition, 3D reconstruction and autonomous driving. Despite the recent advancements in depth prediction from single RGB images with deeper neural networks, the existing approaches do not yield reliable results for practical use. In this work, we propose a neural network with post-optimization, which takes an RGB image and sparse depth samples as input and predicts the complete depth map. We make three major contributions to advance the state-of-the-art: an improved backbone network architecture named EDNet, a semantic edge-weighted loss function and a semantic mesh deformation optimization method. Our evaluation results outperform the existing work consistently on both indoor and outdoor datasets, and it significantly reduces the mean average error by up to 19.5% under the same settings of 200 sparse samples on NYU-Depth-V2 dataset.
Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning

The immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis, preclinical studies, and diagnostic decisions, guiding cancer treatment and investigation of pathogenesis. HER2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist, which typically takes one day to prepare in a laboratory, increasing analysis time and associated costs. Here, we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis, in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs) to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts. A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual HER2 staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.
Cosmology and modified gravitational wave propagation from binary black hole population models

A joint hierarchical Bayesian analysis of the binary black hole (BBH) mass function, merger rate evolution and cosmological parameters can be used to extract information on both the cosmological and population parameters. We extend this technique to include the effect of modified gravitational wave (GW) propagation. We discuss the constraints on the parameter Ξ0 that describes this phenomenon (with Ξ0 = 1 in General Relativity, GR) using the data from the GWTC-3 catalog. We find the constraints Ξ0 = 1.2 +0.7 −0.7 with a flat prior on Ξ0, and Ξ0 = 1.0 +0.4 −0.8 with a prior uniform in log Ξ0 (68% C.L., maximum posterior and HDI), which only rely on the presence of a feature in the BBH mass distribution around ∼ 30−45M , and are robust to whether or not the event GW190521 is considered an outlier of the population. We then study in more detail the effects of modified GW propagation on population and cosmological analyses for LIGO/Virgo at design sensitivity. We find that, for a given data-taking period, the relative error ∆Ξ0/Ξ0 has a significant dependence on the fiducial value of Ξ0, since the latter has a strong influence on the detection rate. For five years of data, the accuracy ranges from ∼ 10% on Ξ0 when Ξ0 = 1 to ∆Ξ0/Ξ0 ∼ 20% for Ξ0 = 1.8, that represents a large deviation from GR, still consistent with current limits and predicted by viable cosmological models. For the Hubble parameter, we forecast an accuracy of ∆H0/H0 ∼ 20%, and an accuracy on H(z) of ∼ 7% at a pivot redshift z∗ ∼ 0.8; this updates the results found in previous studies, by making use of a population model compatible with current observations, and is about a factor 2 worse, in twice the observing time. We finally show that, if Nature is described by a modified gravity theory with a large deviation from the GR value Ξ0 = 1, such as Ξ0 = 1.8, analysing the data assuming GR produces a significant bias in the inferred values of the mass scales, Hubble constant, and particularly the BBH merger rate.
Compressive Gate Set Tomography

Flexible characterization techniques that identify and quantify experimental imperfections under realistic assumptions are crucial for the development of quantum computers. Gate set tomography is a characterization approach that simultaneously and self-consistently extracts a tomographic description of the implementation of an entire set of quantum gates, as well as the initial state and measurement, from experimental data. Obtaining such a detailed picture of the experimental implementation is associated with high requirements on the number of sequences and their design, making gate set tomography a challenging task even for only two qubits. In this work, we show that low-rank approximations of gate sets can be obtained from significantly fewer gate sequences and that it is sufficient to draw them randomly. Such tomographic information is needed for the crucial task of dealing with coherent noise. To this end, we formulate the data processing problem of gate set tomography as a rank-constrained tensor completion problem. We provide an algorithm to solve this problem while respecting the usual positivity and normalization constraints of quantum mechanics by using second-order geometrical optimization methods on the complex Stiefel manifold. Besides the reduction in sequences, we demonstrate numerically that the algorithm does not rely on structured gate sets or an elaborate circuit design to robustly perform gate set tomography. Therefore, it is more flexible than traditional approaches. We also demonstrate how coherent errors in shadow estimation protocols can be mitigated using estimates from gate set tomography.
Global convergence of ResNets: From finite to infinite width using linear parameterization

Overparametrization is a key factor in the absence of convexity to explain global convergence of gradient descent (GD) for neural networks. Beside the well studied lazy regime, infinite width (mean field) analysis has been developed for shallow networks, using on convex optimization technics. To bridge the gap between the lazy and mean field regimes, we study Residual Networks (ResNets) in which the residual block has linear parametrization while still being nonlinear. Such ResNets admit both infinite depth and width limits, encoding residual blocks in a Reproducing Kernel Hilbert Space (RKHS). In this limit, we prove a local Polyak-Lojasiewicz inequality. Thus, every critical point is a global minimizer and a local convergence result of GD holds, retrieving the lazy regime. In contrast with other mean-field studies, it applies to both parametric and non-parametric cases under an expressivity condition on the residuals. Our analysis leads to a practical and quantified recipe: starting from a universal RKHS, Random Fourier Features are applied to obtain a finite dimensional parameterization satisfying with high-probability our expressivity condition.
On the variational method for Euclidean quantum fields in infinite volume

We investigate the infinite volume limit of the variational description of Euclidean quantum fields introduced in a previous work. Focussing on two dimensional theories for simplicity, we prove in details how to use the variational approach to obtain tightness of $\varphi^4_2$ without cutoffs and a corresponding large deviation principle for any infinite volume limit. Any infinite volume measure is described via a forward--backwards stochastic differential equation in weak form (wFBSDE). Similar considerations apply to more general $P (\varphi)_2$ theories. We consider also the $\exp (\beta \varphi)_2$ model for $\beta^2<8 \pi$ (the so called full $L^1$ regime) and prove uniqueness of the infinite volume limit and a variational characterization of the unique infinite volume measure. The corresponding characterization for $P (\varphi)_2$ theories is lacking due to the difficulty of studying the stability of the wFBSDE against local perturbations.
An Integrative Analysis of the HD 219134 Planetary System and the Inner solar system: Extending DYNAMITE with Enhanced Orbital Dynamical Stability Criteria

Planetary architectures remain unexplored for the vast majority of exoplanetary systems, even among the closest ones, with potentially hundreds of planets still “hidden” from our knowledge. Dynamite is a powerful software package that can predict the presence and properties of these yet-undiscovered planets. We have significantly expanded the integrative capabilities of Dynamite, which now allows for (i) planets of unknown inclinations alongside planets of known inclinations, (ii) population statistics and model distributions for the eccentricity of planetary orbits, and (iii) three different dynamical stability criteria. We demonstrate the new capabilities with a study of the HD 219134 exoplanet system consisting of four confirmed planets and two likely candidates, where five of the likely planets and candiates are Neptune-sized or below with orbital periods less than 100 days. By integrating the known data for the HD 219134 planetary system with contextual and statistical exoplanet population information, we tested different system architecture hypotheses to determine their likely dynamical stability. Our results provide support for the planet candidates, and we predict at least two additional planets in this system. We also deploy Dynamite on analogs of the inner solar system by excluding Venus or Earth from the input parameters to test Dynamite's predictive power. Our analysis finds that the system remains stable while also recovering the excluded planets, demonstrating the increasing capability of Dynamite to accurately and precisely model the parameters of additional planets in multiplanet systems.
Density and pseudo-spin rotons in a bilayer of soft-core bosons

We study the dynamics of a bilayer system of bosons with repulsive soft-core Rydberg-dressed interactions within the mean-field Bogoliubov-de Gennes approximation. We find roton minima in both symmetric and asymmetric collective density modes of the symmetric bilayer. Depending on the density of bosons in each layer and the spacing between two layers, the homogeneous superfluid phase becomes unstable in either (or both) of these two channels, leading to density and pseudo-spin-density wave instabilities in the system. Breaking the symmetry between two layers, either with a finite counterflow or a density imbalance renormalizes the dispersion of collective modes and makes the system more susceptible to density-wave instability.
Interaction-Aware Sensitivity Analysis for Aerodynamic Optimization Results using Information Theory

An important issue during an engineering design process is to develop an understanding which design parameters have the most influence on the performance. Especially in the context of optimization approaches this knowledge is crucial in order to realize an efficient design process and achieve high-performing results. Information theory provides powerful tools to investigate these relationships because measures are model-free and thus also capture non-linear relationships, while requiring only minimal assumptions on the input data. We therefore propose to use recently introduced information-theoretic methods and estimation algorithms to find the most influential input parameters in optimization results. The proposed methods are in particular able to account for interactions between parameters, which are often neglected but may lead to redundant or synergistic contributions of multiple parameters. We demonstrate the application of these methods on optimization data from aerospace engineering, where we first identify the most relevant optimization parameters using a recently introduced information-theoretic feature-selection algorithm that accounts for interactions between parameters. Second, we use the novel partial information decomposition (PID) framework that allows to quantify redundant and synergistic contributions between selected parameters with respect to the optimization outcome to identify parameter interactions. We thus demonstrate the power of novel information-theoretic approaches in identifying relevant parameters in optimization runs and highlight how these methods avoid the selection of redundant parameters, while detecting interactions that result in synergistic contributions of multiple parameters.
Transport in the emergent Bose liquid: Bad metal, strange metal, and weak insulator, all in one system

Non-saturating high-temperature resistivity ("bad metal"), T-linear low-temperature resistivity ("strange metal"), and a crossover to activation-free growth of the resistivity in the low-temperature limit ("weak insulator") are among the most exotic behaviors widely observed in many strongly correlated materials for decades that defy the standard Fermi liquid description of solids. Here we investigate these puzzling behaviors by computing temperature-dependent optical conductivity of an emergent Bose liquid and find that it reproduces all the unexplained features of the experiments, including a featureless continuum and a well-known mid-infrared peak. Amazingly and with physically intuitive mechanisms, the corresponding doping- and temperature-dependent resistivity displays the bad metal and strange metal simultaneously and sometimes weak insulating behaviors as well. The unification of all these non-Fermi liquid behaviors in a single model suggests that a new quantum state of matter, namely the emergent Bose liquid, will guide the development of the next generation of solid state physics.
Representing 3D Shapes with Probabilistic Directed Distance Fields

Differentiable rendering is an essential operation in modern vision, allowing inverse graphics approaches to 3D understanding to be utilized in modern machine learning frameworks. Explicit shape representations (voxels, point clouds, or meshes), while relatively easily rendered, often suffer from limited geometric fidelity or topological con-straints. On the other hand, implicit representations (occu-pancy, distance, or radiance fields) preserve greater fidelity, but suffer from complex or inefficient rendering processes, limiting scalability. In this work, we endeavour to address both shortcomings with a novel shape representation that allows fast differentiable rendering within an implicit ar-chitecture. Building on implicit distance representations, we define Directed Distance Fields (DDFs), which map an oriented point (position and direction) to surface visibility and depth. Such a field can render a depth map with a single forward pass per pixel, enable differential surface geometry extraction (e.g., surface normals and curvatures) via network derivatives, be easily composed, and permit extraction of classical unsigned distance fields. Using probabilistic DDFs (PDDFs), we show how to model inherent discontinuities in the underlying field. Finally, we apply our method to fitting single shapes, unpaired 3D-aware generative image modelling, and single-image 3D reconstruction tasks, showcasing strong performance with simple architectural components via the versatility of our representation.
Where Did the Outskirts Go? Outer Stellar Halos as a Sensitive Probe of Supernova Feedback

A recent comparison by Merritt of simulated and observed Milky Way–mass galaxies has identified a significant tension between the outskirts (r > 20 kpc) of the stellar halos in simulated and observed galaxies. Using observations from the Dragonfly telescope and simulated galaxies from the Illustris-TNG100 project, Merritt found that the outskirts of stellar halos in simulated galaxies have surface densities 1–2 dex higher than those of observed galaxies. In this paper, we compare two suites of 15 simulated Milky Way–like galaxies, each drawn from the same initial conditions, simulated with the same hydrodynamical code, but with two different models for feedback from supernovae. We find that the McMaster Unbiased Galaxy Simulations (MUGS), which use an older “delayed-cooling” model for feedback, also produce too much stellar mass in the outskirts of the halo, with median surface densities well above observational constraints. The MUGS2 simulations, which instead use a new, physically motivated “superbubble” model for stellar feedback, have 1–2 dex lower outer stellar halo masses and surface densities. The MUGS2 simulations generally match both the median surface density profile as well as the scatter in stellar halo surface density profiles seen in observed stellar halos. We conclude that there is no “missing outskirts” problem in cosmological simulations, provided that supernova feedback is modeled in a way that allows it to efficiently regulate star formation in the low-mass progenitor environments of stellar halo outskirts.
Model Selection for Generic Reinforcement Learning

We address the problem of model selection for the finite horizon episodic Reinforcement Learning (RL) problem where the transition kernel $P^*$ belongs to a family of models $\mathcal{P}^*$ with finite metric entropy. In the model selection framework, instead of $\mathcal{P}^*$, we are given $M$ nested families of transition kernels $\cP_1 \subset \cP_2 \subset \ldots \subset \cP_M$. We propose and analyze a novel algorithm, namely \emph{Adaptive Reinforcement Learning (General)} (\texttt{ARL-GEN}) that adapts to the smallest such family where the true transition kernel $P^*$ lies. \texttt{ARL-GEN} uses the Upper Confidence Reinforcement Learning (\texttt{UCRL}) algorithm with value targeted regression as a blackbox and puts a model selection module at the beginning of each epoch. Under a mild separability assumption on the model classes, we show that \texttt{ARL-GEN} obtains a regret of $\Tilde{\mathcal{O}}(d_{\mathcal{E}}^*H^2+\sqrt{d_{\mathcal{E}}^* \mathbb{M}^* H^2 T})$, with high probability, where $H$ is the horizon length, $T$ is the total number of steps, $d_{\mathcal{E}}^*$ is the Eluder dimension and $\mathbb{M}^*$ is the metric entropy corresponding to $\mathcal{P}^*$. Note that this regret scaling matches that of an oracle that knows $\mathcal{P}^*$ in advance. We show that the cost of model selection for \texttt{ARL-GEN} is an additive term in the regret having a weak dependence on $T$. Subsequently, we remove the separability assumption and consider the setup of linear mixture MDPs, where the transition kernel $P^*$ has a linear function approximation. With this low rank structure, we propose novel adaptive algorithms for model selection, and obtain (order-wise) regret identical to that of an oracle with knowledge of the true model class.
SARS-CoV-2 seroprevalence in health care workers from 10 hospitals in Quebec, Canada: a cross-sectional study

Background: The COVID-19 pandemic has disproportionately affected health care workers. We sought to estimate SARS-CoV-2 seroprevalence among hospital health care workers in Quebec, Canada, after the first wave of the pandemic and to explore factors associated with SARS-CoV-2 seropositivity. Methods: Between July 6 and Sept. 24, 2020, we enrolled health care workers from 10 hospitals, including 8 from a region with a high incidence of COVID-19 (the Montréal area) and 2 from low-incidence regions of Quebec. Eligible health care workers were physicians, nurses, orderlies and cleaning staff working in 4 types of care units (emergency department, intensive care unit, COVID-19 inpatient unit and non-COVID-19 inpatient unit). Participants completed a questionnaire and underwent SARS-CoV-2 serology testing. We identified factors independently associated with higher seroprevalence. Results: Among 2056 enrolled health care workers, 241 (11.7%) had positive SARS-CoV-2 serology. Of these, 171 (71.0%) had been previously diagnosed with COVID-19. Seroprevalence varied among hospitals, from 2.4% to 3.7% in low-incidence regions to 17.9% to 32.0% in hospitals with outbreaks involving 5 or more health care workers. Higher seroprevalence was associated with working in a hospital where outbreaks occurred (adjusted prevalence ratio 4.16, 95% confidence interval [CI] 2.63–6.57), being a nurse or nursing assistant (adjusted prevalence ratio 1.34, 95% CI 1.03–1.74) or an orderly (adjusted prevalence ratio 1.49, 95% CI 1.12–1.97), and Black or Hispanic ethnicity (adjusted prevalence ratio 1.41, 95% CI 1.13–1.76). Lower seroprevalence was associated with working in the intensive care unit (adjusted prevalence ratio 0.47, 95% CI 0.30–0.71) or the emergency department (adjusted prevalence ratio 0.61, 95% CI 0.39–0.98). Interpretation: Health care workers in Quebec hospitals were at high risk of SARS-CoV-2 infection, particularly in outbreak settings. More work is needed to better understand SARS-CoV-2 transmission dynamics in health care settings.
Two-loop controller for maximizing performance of a grid-connected photovoltaic-fuel cell hybrid power plant

Maximizing performance of a grid-connected photovoltaic (PV)-fuel cell hybrid system by use of a two-loop controller is discussed. One loop is a neural network controller for maximum power point tracking, which extracts maximum available solar power from PV arrays under varying conditions of insolation, temperature, and system load. A real/reactive power controller (RRPC) is the other loop. The RRPC achieves the system's requirements for real and reactive powers by controlling incoming fuel to fuel cell stacks as well as switching control signals to a power conditioning subsystem. Results of time-domain simulations prove not only the effectiveness of the proposed computer models of the two-loop controller but also its applicability for use in stability analysis of the hybrid power plant.
Model-driven PID control system in single-loop controller

PID control is widely used as a basic control technology in industries today, but tuning of PID control systems is not always easy. Based on model-driven control concept, we developed a model-driven PID control system, named MD PID controller, combining with PD local feedback, IMC and set point filter. This paper provides a brief introduction of new model driven two-degrees of freedom PID control system, named MD TDOF PID controller, and shows some case studies results. We have confirmed that MD TDOF PID can show better control results compared with conventional PID control.