Miracle-dz/UniFAR-FTUs huggingface dataset:
Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3D Supervision

Learning-based 3D reconstruction methods have shown impressive results. However, most methods require 3D supervision which is often hard to obtain for real-world datasets. Recently, several works have proposed differentiable rendering techniques to train reconstruction models from RGB images. Unfortunately, these approaches are currently restricted to voxel- and mesh-based representations, suffering from discretization or low resolution. In this work, we propose a differentiable rendering formulation for implicit shape and texture representations. Implicit representations have recently gained popularity as they represent shape and texture continuously. Our key insight is that depth gradients can be derived analytically using the concept of implicit differentiation. This allows us to learn implicit shape and texture representations directly from RGB images. We experimentally show that our single-view reconstructions rival those learned with full 3D supervision. Moreover, we find that our method can be used for multi-view 3D reconstruction, directly resulting in watertight meshes.
Improving Heart Rate Estimation on Consumer Grade Wrist-Worn Device Using Post-Calibration Approach

The technological advancement in wireless health monitoring through the direct contact of the skin allows the development of light-weight wrist-worn wearable devices to be equipped with different sensors such as photoplethysmography (PPG) sensors. However, the motion artifact (MA) is possible to occur during daily activities. In this study, we attempted to perform a post-calibration of the heart rate (HR) estimation during the three possible states of average daily activity (resting, laying down, and intense treadmill activity states) in 29 participants (130 minutes/person) on four popular wearable devices: Fitbit Charge HR, Apple Watch Series 4, TicWatch Pro, and Empatica E4. In comparison to the standard measurement (HRECG), HR provided by Fitbit Charge HR (HRFitbit) yielded the highest error of 3.26±0.34 bpm in resting, 2.33±0.23 bpm in laying down, 9.53±1.47 bpm in intense treadmill activity states, and 5.02±0.64 bpm in all states combined among the four chosen devices. Following our improving HR estimation model with rolling windows as feature (HRR), the mean absolute error (MAE) was significantly reduced by 33.44% in resting, 15.88% in laying down, 9.55% in intense treadmill activity states, and 18.73% in all states combined. This demonstrates the feasibility of our proposed methods in order to correct and provide HR monitoring post-calibrated with high accuracy, raising further awareness of individual fitness in the daily application.
The size of graphs with restricted rainbow $2$-connection number

Let $k$ be a positive integer, and $G$ be a $k$-connected graph. An edge-coloured path is \emph{rainbow} if all of its edges have distinct colours. The \emph{rainbow $k$-connection number} of $G$, denoted by $rc_k(G)$, is the minimum number of colours in an edge-colouring of $G$ such that, any two vertices are connected by $k$ internally vertex-disjoint rainbow paths. The function $rc_k(G)$ was introduced by Chartrand, Johns, McKeon and Zhang in 2009, and has since attracted significant interest. Let $t_k(n,r)$ denote the minimum number of edges in a $k$-connected graph $G$ on $n$ vertices with $rc_k(G)\le r$. Let $s_k(n,r)$ denote the maximum number of edges in a $k$-connected graph $G$ on $n$ vertices with $rc_k(G)\ge r$. The functions $t_1(n,r)$ and $s_1(n,r)$ have previously been studied by various authors. In this paper, we study the functions $t_2(n,r)$ and $s_2(n,r)$. We determine bounds for $t_2(n,r)$ which imply that $t_2(n,2)=(1+o(1))n\log_2 n$, and $t_2(n,r)$ is linear in $n$ for $r\ge 3$. We also provide some remarks about the function $s_2(n,r)$.
Near-surface dynamics of a gas bubble collapsing above a crevice

Abstract The impact of a collapsing gas bubble above rigid, notched walls is considered. Such surface crevices and imperfections often function as bubble nucleation sites, and thus have a direct relation to cavitation-induced erosion and damage structures. A generic configuration is investigated numerically using a second-order accurate compressible multi-component flow solver in a two-dimensional axisymmetric coordinate system. Results show that the crevice geometry has a significant effect on the collapse dynamics, jet formation, subsequent wave dynamics and interactions. The wall-pressure distribution associated with erosion potential is a direct consequence of development and intensity of these flow phenomena.
Statistical Significance in High-dimensional Linear Mixed Models

This paper develops an inferential framework for high-dimensional linear mixed effect models. Such models are suitable, e.g., when collecting n repeated measurements for M subjects. We consider a scenario where the number of fixed effects p is large (and may be larger than M), but the number of random effects q is small. Our framework is inspired by a recent line of work that proposes de-biasing penalized estimators to perform inference for high-dimensional linear models with fixed effects only. In particular, we demonstrate how to correct a 'naive' ridge estimator to build asymptotically valid confidence intervals for mixed effect models. We validate our theoretical results with numerical experiments that show that our method can successfully account for the correlation induced by the random effects. For a practical demonstration we consider a riboflavin production dataset that exhibits group structure, and show that conclusions drawn using our method are consistent with those obtained on a similar dataset without group structure.
Few-shot Learning with Contextual Cueing for Object Recognition in Complex Scenes

Few-shot Learning aims to recognize new concepts from a small number of training examples. Recent work mainly tackle this problem by improving visual features, feature transfer and meta-training algorithms. In this work, we propose to explore a complementary direction by using scene context semantics to learn and recognize new concepts more easily. Whereas a few visual examples cannot cover all intra-class variations, contextual cueing offers a complementary signal to classify instances with unseen features or ambiguous objects. More specifically, we propose a Class-conditioned Context Attention Module (CCAM) that learns to weight the most important context elements while learning a particular concept. We additionally propose a flexible gating mechanism to ground visual class representations in context semantics. We conduct extensive experiments on Visual Genome dataset, and we show that compared to a visual-only baseline, our model improves top-1 accuracy by 20.47% and 9.13% in 5-way 1-shot and 5-way 5-shot, respectively; and by 20.42% and 12.45% in 20-way 1-shot and 20-way 5-shot, respectively.
Asymmetric Generative Adversarial Networks for Image-to-Image Translation

State-of-the-art models for unpaired image-to-image translation with Generative Adversarial Networks (GANs) can learn the mapping from the source domain to the target domain using a cycle-consistency loss. The intuition behind these models is that if we translate from one domain to the other and back again we should arrive at where we started. However, existing methods always adopt a symmetric network architecture to learn both forward and backward cycles. Because of the task complexity and cycle input difference between the source and target image domains, the inequality in bidirectional forward-backward cycle translations is significant and the amount of information between two domains is different. In this paper, we analyze the limitation of the existing symmetric GAN models in asymmetric translation tasks, and propose an AsymmetricGAN model with both translation and reconstruction generators of unequal sizes and different parameter-sharing strategy to adapt to the asymmetric need in both unsupervised and supervised image-to-image translation tasks. Moreover, the training stage of existing methods has the common problem of model collapse that degrades the quality of the generated images, thus we explore different optimization losses for better training of AsymmetricGAN, and thus make image-to-image translation with higher consistency and better stability. Extensive experiments on both supervised and unsupervised generative tasks with several publicly available datasets demonstrate that the proposed AsymmetricGAN achieves superior model capacity and better generation performance compared with existing GAN models. To the best of our knowledge, we are the first to investigate the asymmetric GAN framework on both unsupervised and supervised image-to-image translation tasks. The source code, data and trained models are available at this https URL.
Accessibility measure for eternal inflation: dynamical criticality and higgs metastability

We propose a new measure for eternal inflation, based on search optimization and first-passage statistics. This work builds on the dynamical selection mechanism for vacua based on search optimization proposed recently by the author and Parrikar. The approach is motivated by the possibility that eternal inflation has unfolded for a finite time much shorter than the exponentially long mixing time for the landscape. The proposed accessibility measure assigns greater weight to vacua that are accessed efficiently under time evolution. It is the analogue of the closeness centrality index widely used in network science. The proposed measure enjoys a number of desirable properties. It is independent of initial conditions and oblivious to physical vs comoving weighing of pocket universes. Importantly, the proposed measure makes concrete and testable predictions that are largely independent of anthropic reasoning. Firstly, it favors vacua residing in regions of the landscape with funnel-like topography, akin to the energy landscape of naturally-occurring proteins. Secondly, it favors regions of the landscape that are tuned at dynamical criticality, with vacua having an average lifetime of order the de Sitter Page time. Thus the predicted lifetime of our universe is of order its Page time, ∼ 10130 years, which is compatible with Standard Model estimates for electroweak metastability. Relatedly, the supersymmetry breaking scale should be high, at least 1010 GeV. The discovery of beyond-the-Standard Model particles at the Large Hadron Collider or future accelerators, including low-scale supersymmetry, would rule out the possibility that our vacuum lies in an optimal region of the landscape. The present framework suggests a correspondence between the near-criticality of our universe and dynamical critical phenomena on the string landscape.
Disentanglement based Active Learning

We propose Disentanglement based Active Learning (DAL), a new active learning technique based on self-supervision which leverages the concept of disentanglement. Instead of requesting labels from human oracle, our method automatically labels majority of the datapoints, thus drastically reducing the human labeling budget in Generative Adversarial Net (GAN) based active learning approaches. The proposed method uses Information Maximizing Generative Adversarial Nets (InfoGAN) to learn disentangled class category representations. Disagreement between active learner predictions and InfoGAN labels decides if the datapoints need to be human labeled. We also introduce a label correction mechanism which aims to filter out label noise that occurs due to automatic labeling. Results on three benchmark datasets for image classification task demonstrate that our method achieves better performance compared to existing GAN based active learning approaches.
Is π Men c’s Atmosphere Hydrogen-dominated? Insights from a Non-detection of H i Lyα Absorption

Constraining the composition of super-Earth-to-sub-Neptune-sized planets is a priority in order to understand the processes of planetary formation and evolution. π Men c represents a unique target for the atmospheric and compositional characterization of such planets because it is strongly irradiated and its bulk density is consistent with abundant H2O. We searched for hydrogen from photodissociating H2/H2O in π Men c's upper atmosphere through H i Lyα transmission spectroscopy with the Hubble Space Telescope’s Space Telescope Imaging Spectrograph, but did not detect it. We set 1σ (3σ) upper limits for the effective planet-to-star size ratio RLyα/R⋆ = 0.13 (0.24) and 0.12 (0.20) at velocities [−215, −91] km s−1 and [+57, +180] km s−1, respectively. We reconstructed the stellar spectrum, and estimate that π Men c receives about 1350 erg cm−2 s−1 of 5–912 Å energy, enough to cause rapid atmospheric escape. An interesting scenario to explain the non-detection is that π Men c's atmosphere is dominated by H2O or other heavy molecules rather than H2/He. According to our models, abundant oxygen results in less extended atmospheres, which transition from neutral to ionized hydrogen closer to the planet. We compare our non-detection to other detection attempts, and tentatively identify two behaviors: planets with densities ≲2 g cm−3 (and likely hydrogen-dominated atmospheres) result in H i Lyα absorption, whereas planets with densities ≳3 g cm−3 (and plausibly non-hydrogen-dominated atmospheres) do not result in measurable absorption. Investigating a sample of strongly irradiated sub-Neptunes may provide some statistical confirmation if it is shown that they do not generally develop extended atmospheres.
Hot WHIM counterparts of FUV O VI absorbers: Evidence in the line-of-sight towards quasar 3C 273

Aims. We explore the high spectral resolution X-ray data towards the quasar 3C 273 to search for signals of hot (∼106−7 K) X-ray-absorbing gas co-located with two established intergalactic far-ultraviolet (FUV) O VI absorbers. Methods. We analyze the soft X-ray band grating data of all XMM-Newton and Chandra instruments to search for the hot phase absorption lines at the FUV predicted redshifts. The viability of potential line detections is examined by adopting the constraints of a physically justified absorption model. The WHIM hypothesis is investigated with a complementary 3D galaxy distribution analysis and by detailed comparison of the measurement results to the WHIM properties in the EAGLE cosmological, hydrodynamical simulation. Results. At one of the examined FUV redshifts, z = 0.09017 ± 0.00003, we measured signals of two hot ion species, O VIII and Ne IX, with a 3. 9σ combined significance level. While the absorption signal is only marginally detected in individual co-added spectra, considering the line features in all instruments collectively and assuming collisional equilibrium for absorbing gas, we were able to constrain the temperature (kT = 0.26 ± 0.03 keV) and the column density (NH × Z⊙/Z = 1.3−0.5+0.6 × 1019 cm−2) of the absorber. Thermal analysis indicates that FUV and X-ray absorption relate to different phases, with estimated temperatures, TFUV ≈ 3 × 105, and, TX − ray ≈ 3 × 106 K. These temperatures match the EAGLE predictions for WHIM at the FUV/X-ray measured Nion-ranges. We detected a large scale galactic filament crossing the sight-line at the redshift of the absorption, linking the absorption to this structure. Conclusions. This study provides observational insights into co-existing warm and hot gas within a WHIM filament and estimates the ratio of the hot and warm phases. Because the hot phase is thermally distinct from the O VI gas, the estimated baryon content of the absorber is increased, conveying the promise of X-ray follow-up studies of FUV detected WHIM in refining the picture of the missing baryons.
Q-ball decay through A-term in the gauge-mediated SUSY breaking scenario

Q-balls are non-topological solitons whose classical stability is ensured by a global U(1) charge. In particular, Q-balls are produced in the framework of the Affleck-Dine baryogenesis where U(1) charge is the baryon number. Since some type of Q-balls is stable against quantum decay into nucleons, it can work as the dark matter. When the dark matter Q-balls are captured into the neutron star, they absorb the surrounding neutrons and grow to consume all the neutrons, which leads to a stringent constraint on the Q-ball dark matter. However, the Q-ball growth stops due to the U(1) breaking A-term. We revisit the constraint by the neutron star for the gauge-mediation type and the new type Q-ball, and find the allowed parameter space, in which Q-balls work as the dark matter.
Estimation of Auction Models with Shape Restrictions

We introduce several new estimation methods that leverage shape constraints in auction models to estimate various objects of interest, including the distribution of a bidder's valuations, the bidder's ex ante expected surplus, and the seller's counterfactual revenue. The basic approach applies broadly in that (unlike most of the literature) it works for a wide range of auction formats and allows for asymmetric bidders. Though our approach is not restrictive, we focus our analysis on first--price, sealed--bid auctions with independent private valuations. We highlight two nonparametric estimation strategies, one based on a least squares criterion and the other on a maximum likelihood criterion. We also provide the first direct estimator of the strategy function. We establish several theoretical properties of our methods to guide empirical analysis and inference. In addition to providing the asymptotic distributions of our estimators, we identify ways in which methodological choices should be tailored to the objects of their interest. For objects like the bidders' ex ante surplus and the seller's counterfactual expected revenue with an additional symmetric bidder, we show that our input--parameter--free estimators achieve the semiparametric efficiency bound. For objects like the bidders' inverse strategy function, we provide an easily implementable boundary--corrected kernel smoothing and transformation method in order to ensure the squared error is integrable over the entire support of the valuations. An extensive simulation study illustrates our analytical results and demonstrates the respective advantages of our least--squares and maximum likelihood estimators in finite samples. Compared to estimation strategies based on kernel density estimation, the simulations indicate that the smoothed versions of our estimators enjoy a large degree of robustness to the choice of an input parameter.
More Data Can Hurt for Linear Regression: Sample-wise Double Descent

In this expository note we describe a surprising phenomenon in overparameterized linear regression, where the dimension exceeds the number of samples: there is a regime where the test risk of the estimator found by gradient descent increases with additional samples. In other words, more data actually hurts the estimator. This behavior is implicit in a recent line of theoretical works analyzing "double-descent" phenomenon in linear models. In this note, we isolate and understand this behavior in an extremely simple setting: linear regression with isotropic Gaussian covariates. In particular, this occurs due to an unconventional type of bias-variance tradeoff in the overparameterized regime: the bias decreases with more samples, but variance increases.
Discreteness effects, N-body chaos and the onset of radial-orbit instability

We study the stability of a family of spherical equilibrium models of self-gravitating systems, the so-called γ models with Osipkov–Merritt velocity anisotropy, by means of N-body simulations. In particular, we analyse the effect of self-consistent N-body chaos on the onset of radial-orbit instability. We find that degree of chaoticity of the system associated with its largest Lyapunov exponent Λmax has no appreciable relation with the stability of the model for fixed density profile and different values of radial velocity anisotropy. However, by studying the distribution of the Lyapunov exponents λm of the individual particles in the single-particle phase space, we find that more anisotropic systems have a larger fraction of orbits with larger λm.
A Cross-sectional Study of Postpartum Depressive Symptoms and Infant Weight Outcomes: Do Well-Child Visits make a Difference?

A Cross-sectional Study of Postpartum Depressive Symptoms and Infant Weight Outcomes: Do Well-Child Visits make a Difference? Maternal postpartum depression affects 10% to 20% of new mothers, but little research has examined its relationship with weight outcomes during infancy and whether clinical interventions can mitigate this relationship. This study investigated the associations of postpartum depressive symptoms with infants’ weight-for-length z-scores, obesity, and overweight at 9 months of age; and whether the number of well-child visits moderated these associations.
Joint Learning of Generative Translator and Classifier for Visually Similar Classes

In this paper, we propose a Generative Translation Classification Network (GTCN) for improving visual classification accuracy in settings where classes are visually similar and data is scarce. For this purpose, we propose joint learning from a scratch to train a classifier and a generative stochastic translation network end-to-end. The translation network is used to perform on-line data augmentation across classes, whereas previous works have mostly involved domain adaptation. To help the model further benefit from this data-augmentation, we introduce an adaptive fade-in loss and a quadruplet loss. We perform experiments on multiple datasets to demonstrate the proposed method’s performance in varied settings. Of particular interest, training on 40% of the dataset is enough for our model to surpass the performance of baselines trained on the full dataset. When our architecture is trained on the full dataset, we achieve comparable performance with state-of-the-art methods despite using a light-weight architecture.
Lorentzian quantum cosmology with $R^2$ correction

Quantum mechanical transition amplitudes directly tells the probability of each transition and which one is more favourable. Path-integrals offers a systematic methodology to compute this quantum mechanical process in a consistent manner. Although it is not complicated in simple quantum mechanical system but defining path-integral legitimately becomes highly nontrivial in the context of quantum-gravity, where apart from usual issues of renormalizability, regularisation, measure, gauge-fixing, boundary conditions, one still has to define the sensible integration contour for convergence. Picard-Lefschetz (PL) theory offers a unique way to find a contour of integration based on the analysis of saddle points and the steepest descent/ascent flow lines in the complex plane. In this paper we make use of PL-theory to investigate Lorentzian quantum cosmology where the gravity gets modified in the ultraviolet with the $R^2$ corrections. We approach the problem perturbatively and compute the transition amplitude in the saddle point approximation to first order in higher-derivative coupling. This perturbative approximation is valid in certain regimes but the approximation cannot be used to address issues of very early Universe or no-boundary proposal.